From d53f44c29d9ef600cc70d2916991975358b2f549 Mon Sep 17 00:00:00 2001 From: Jeremy Price Date: Sun, 21 May 2023 14:43:22 -0400 Subject: [PATCH] added participant description --- docs/index.html | 580 +++++++++++++++++++++-------------------- docs/search.json | 9 +- index.qmd | 108 ++++++-- outputs/pdtg_badge.pdf | Bin 66917 -> 66917 bytes 4 files changed, 403 insertions(+), 294 deletions(-) diff --git a/docs/index.html b/docs/index.html index 91554c8..fcadd11 100644 --- a/docs/index.html +++ b/docs/index.html @@ -291,29 +291,29 @@

Identifying the Me

This overall score is similar to the Altmetric Attention Score in that it is a weighted count. The overall score increases

Each of these categories and their components are described in some detail below. The idea, the math, the concepts, are all licensed under a CC BY-SA 4.0 license, so you are welcome to take these ideas and adapt them to your particular needs and preferences (as long as you give credit where credit is due and share your adapted work alike).

The code itself is licensed similarly under an MIT License.
-
-

Participants

-

People Involved

+
+

Participants Component Score

+

The Participants Component Score represents the people involved in the project. Included in this score are the number of people involved, representation of marginalized community members and identities, and representation of intersectional identities and community membership.

-
- @@ -786,33 +786,57 @@

Participants

Number of Participants

-

How many participants involved? This is \(p_n\). For this example, there are 6 participants.

+

How many participants involved? This is \(p_n\).

+

For the Professional Development project at one of the elementary schools, there were 70 directly teachers involved. For Family as Faculty as an Infrastructure, the participants consisted of the following:

+
    +
  • 1 School Principal
    • +
  • 2 Institutional Leaders
    • +
  • 3 University Faculty and Staff (not part of the project team)
    • +
  • 4 Neighborhood Participants
    • +
  • 5 Family Leaders
    • +
  • 5 Cooperating Teachers
    • +
  • 30 Undergraduate Students
    • +
+

This is a total of 50 direct participants.

- +
tg_number_of_participants <- 70
+fafi_number_of_participants <- 50
+number_of_participants <- 7

Marginalized Proportions Score

-

How many of the participants represent and come from marginalized identities and communities? This is \(p_m\).

-

Also, are you working directly with these marginalized participants, or are you working with others (such as teachers or health care workers) who are working with these identities and communities?

+

What proportion of the participants represent and come from marginalized identities and communities? This is \(p_m\).

+

You can consider the direct population you are working with, or you can consider your target population who may be one degree removed when deciding on the \(p_m\) rating. If you consider your target, rather than direct, population, there is a “penalty” or damping for doing so in the form of a marginalized proportions coefficient, \(\beta_m\). If you are you working directly with the population, \(\beta_m\) is 1, or no damping If you are working with, for example, teachers or health care workers who will work with your target population, then \(\beta_m\) is 0.85 to dampen the score.

+

For the Professional Development project, the students in the school–the actual target population–predominantly represent marginalized identities and communities, so \(p_m\) is 0.9. The students are one degree away from the teachers, however, so \(\beta_m\) is 0.85 to dampen the component score. For Family as Faculty as an Infrastructure, just over half of the participants represent marginalized identities and communities, so \(p_m\) is 0.75. We are considering this population directly (we can consider ripple effects later on) so \(\beta_m\) is set to 1 so there is no damping of the score.

- +
marginalized_proportions_value <- 1
+marginalized_proportions_coefficient <- 1
+tg_marginalized_proportions_value <- 0.95
+tg_marginalized_proportions_coefficient <- 0.85
+fafi_marginalized_proportions_value <- 0.75
+fafi_marginalized_proportions_coefficient <- 1
-
-

Intersectionally Marginalized Score

+
+

Intersectionally Marginalized Proportions Score

+

What proportion of the participants represent and come from multiple and intersecting marginalized identities and communities? This is \(p_i\).

+

Just like with the Marginalized Proportions Score, you can consider the direct population you are working with, or you can consider your target population. The Intersectionally Marginalized Coefficient, \(\beta_i\), is the same as above. If you consider your target, rather than direct, population, \(\beta_i\) is 0.85. If you are you working directly with the population, \(\beta_i\) is 1.

+

For the Professional Development project, we will again be considering the intersectionality of the students in the school. Nearly all students represent marginalized identities and communities, so \(p_i\) is 0.9. The students are one degree away so \(\beta_i\) is 0.85 to dampen the component score. For Family as Faculty as an Infrastructure, just over half of the participants represent marginalized identities and communities, so \(p_m\) is 0.75 and \(\beta_i\) is set to 1.

-
-

Calculate Participation Score

+
+

Calculate Participant Component Score

+

The Participant Component Score is calculated through the following equation:

\[ p_s = p_n \left( 1 + \frac{(\beta_m \cdot p_m + \beta_i \cdot p_i)}{2} \right) \]

+

We find that the Professional Development Project has a Participant Index (\(p_i\)) of 0.81 (adjusted) and a full Participant Score (\(p_s\)) of 127. Family as Faculty as an Infrastructure has a Participant Index (\(p_i\)) of 0.75 and a Participant Score (\(p_s\)) of 88.

@@ -821,23 +845,23 @@

Engagement

-
- @@ -1341,23 +1365,23 @@

Infrastructure Score<
-
- @@ -1846,23 +1870,23 @@

Outputs Score

-
- @@ -2351,23 +2375,23 @@

Sustainability

-
- @@ -2854,7 +2878,7 @@

Calculate S

Ripple Effects

-
lambda_ripple <- 0.55
+
lambda_ripple <- 0.55

\[ \eta_r = \lambda_r \cdot \frac{n_r}{\log(d_r + 1)} diff --git a/docs/search.json b/docs/search.json index bdc4017..061027f 100644 --- a/docs/search.json +++ b/docs/search.json @@ -18,7 +18,7 @@ "href": "index.html#participants", "title": "CER-BEANS: Community Engaged Research Balanced Expressions and Assessments with Nuanced Scores", "section": "Participants", - "text": "Participants\nPeople Involved\n\n\n\n\n\n\n \n \n \n marginalized_proportions\n intersectionally_marginalized\n participants_value\n \n \n \n No Marginalized Participants\nNo Intersectionally Marginalized Participants\n0.10\n A Few Marginalized Participants\nA Few Intersectionally Marginalized Participants\n0.25\n Some Marginalized Participants\nSome Intersectionally Marginalized Participants\n0.33\n Just Under Half Marginalized Participants\nJust Under Half Intersectionally Marginalized Participants\n0.40\n About Half Marginalized Participants\nAbout Half Intersectionally Marginalized Participants\n0.50\n Just Over Half Marginalized Participants\nJust Over Half Intersectionally Marginalized Participants\n0.75\n Mostly Marginalized Participants\nMostly Marginalized Intersectionally Participants\n0.85\n Predominantly Marginalized Participants\nPredominantly Intersectionally Marginalized Participants\n0.90\n Nearly All Marginalized Participants\nNearly All Intersectionally Marginalized Participants\n0.95\n All Marginalized Participants\nAll Marginalized Intersectionally Participants\n1.00\n \n \n \n\n\n\n\n\nNumber of Participants\nHow many participants involved? This is \\(p_n\\). For this example, there are 6 participants.\n\n\n\n\n\nMarginalized Proportions Score\nHow many of the participants represent and come from marginalized identities and communities? This is \\(p_m\\).\nAlso, are you working directly with these marginalized participants, or are you working with others (such as teachers or health care workers) who are working with these identities and communities?\n\n\n\n\n\nIntersectionally Marginalized Score\n\n\n\n\n\nCalculate Participation Score\n\\[\np_s = p_n \\left( 1 + \\frac{(\\beta_m \\cdot p_m + \\beta_i \\cdot p_i)}{2} \\right)\n\\]" + "text": "Participants\nPeople Involved\n\n\n\n\n\n\n \n \n \n marginalized_proportions\n intersectionally_marginalized\n participants_value\n \n \n \n No Marginalized Participants\nNo Intersectionally Marginalized Participants\n0.10\n A Few Marginalized Participants\nA Few Intersectionally Marginalized Participants\n0.25\n Some Marginalized Participants\nSome Intersectionally Marginalized Participants\n0.33\n Just Under Half Marginalized Participants\nJust Under Half Intersectionally Marginalized Participants\n0.40\n About Half Marginalized Participants\nAbout Half Intersectionally Marginalized Participants\n0.50\n Just Over Half Marginalized Participants\nJust Over Half Intersectionally Marginalized Participants\n0.75\n Mostly Marginalized Participants\nMostly Marginalized Intersectionally Participants\n0.85\n Predominantly Marginalized Participants\nPredominantly Intersectionally Marginalized Participants\n0.90\n Nearly All Marginalized Participants\nNearly All Intersectionally Marginalized Participants\n0.95\n All Marginalized Participants\nAll Marginalized Intersectionally Participants\n1.00\n \n \n \n\n\n\n\n\nNumber of Participants\nHow many participants involved? This is \\(p_n\\).\nFor the Professional Development project at one of the elementary schools, there were 70 directly teachers involved. For Family as Faculty, the participants consisted of the following:\n\n1 School Principal\n2 Institutional Leaders\n3 University Faculty and Staff (not part of the project team)\n4 Neighborhood Participants\n5 Family Leaders\n5 Cooperating Teachers\n30 Undergraduate Students\n\nThis is a total of 50 direct participants.\n\ntg_number_of_participants <- 70\nfaf_number_of_participants <- 50\nnumber_of_participants <- 7\n\n\n\nMarginalized Proportions Score\nHow many of the participants represent and come from marginalized identities and communities? This is \\(p_m\\).\nAlso, are you working directly with these marginalized participants, or are you working with others (such as teachers or health care workers) who are working with these identities and communities?\n\n\n\n\n\nIntersectionally Marginalized Score\n\n\n\n\n\nCalculate Participation Score\n\\[\np_s = p_n \\left( 1 + \\frac{(\\beta_m \\cdot p_m + \\beta_i \\cdot p_i)}{2} \\right)\n\\]" }, { "objectID": "index.html#engagement", @@ -89,5 +89,12 @@ "title": "CER-BEANS: Community Engaged Research Balanced Expressions and Assessments with Nuanced Scores", "section": "Ripple Effects", "text": "Ripple Effects\n\nlambda_ripple <- 0.55\n\n\\[\n\\eta_r = \\lambda_r \\cdot \\frac{n_r}{\\log(d_r + 1)}\n\\]" + }, + { + "objectID": "index.html#participants-component-score", + "href": "index.html#participants-component-score", + "title": "CER-BEANS: Community Engaged Research Balanced Expressions and Assessments with Nuanced Scores", + "section": "Participants Component Score", + "text": "Participants Component Score\nThe Participants Component Score represents the people involved in the project. Included in this score are the number of people involved, representation of marginalized community members and identities, and representation of intersectional identities and community membership.\n\n\n\n\n\n\n \n \n \n marginalized_proportions\n intersectionally_marginalized\n participants_value\n \n \n \n No Marginalized Participants\nNo Intersectionally Marginalized Participants\n0.10\n A Few Marginalized Participants\nA Few Intersectionally Marginalized Participants\n0.25\n Some Marginalized Participants\nSome Intersectionally Marginalized Participants\n0.33\n Just Under Half Marginalized Participants\nJust Under Half Intersectionally Marginalized Participants\n0.40\n About Half Marginalized Participants\nAbout Half Intersectionally Marginalized Participants\n0.50\n Just Over Half Marginalized Participants\nJust Over Half Intersectionally Marginalized Participants\n0.75\n Mostly Marginalized Participants\nMostly Marginalized Intersectionally Participants\n0.85\n Predominantly Marginalized Participants\nPredominantly Intersectionally Marginalized Participants\n0.90\n Nearly All Marginalized Participants\nNearly All Intersectionally Marginalized Participants\n0.95\n All Marginalized Participants\nAll Marginalized Intersectionally Participants\n1.00\n \n \n \n\n\n\n\n\nNumber of Participants\nHow many participants involved? This is \\(p_n\\).\nFor the Professional Development project at one of the elementary schools, there were 70 directly teachers involved. For Family as Faculty as an Infrastructure, the participants consisted of the following:\n\n1 School Principal\n2 Institutional Leaders\n3 University Faculty and Staff (not part of the project team)\n4 Neighborhood Participants\n5 Family Leaders\n5 Cooperating Teachers\n30 Undergraduate Students\n\nThis is a total of 50 direct participants.\n\ntg_number_of_participants <- 70\nfafi_number_of_participants <- 50\nnumber_of_participants <- 7\n\n\n\nMarginalized Proportions Score\nWhat proportion of the participants represent and come from marginalized identities and communities? This is \\(p_m\\).\nYou can consider the direct population you are working with, or you can consider your target population who may be one degree removed when deciding on the \\(p_m\\) rating. If you consider your target, rather than direct, population, there is a “penalty” or damping for doing so in the form of a marginalized proportions coefficient, \\(\\beta_m\\). If you are you working directly with the population, \\(\\beta_m\\) is 1, or no damping If you are working with, for example, teachers or health care workers who will work with your target population, then \\(\\beta_m\\) is 0.85 to dampen the score.\nFor the Professional Development project, the students in the school–the actual target population–predominantly represent marginalized identities and communities, so \\(p_m\\) is 0.9. The students are one degree away from the teachers, however, so \\(\\beta_m\\) is 0.85 to dampen the component score. For Family as Faculty as an Infrastructure, just over half of the participants represent marginalized identities and communities, so \\(p_m\\) is 0.75. We are considering this population directly (we can consider ripple effects later on) so \\(\\beta_m\\) is set to 1 so there is no damping of the score.\n\nmarginalized_proportions_value <- 1\nmarginalized_proportions_coefficient <- 1\ntg_marginalized_proportions_value <- 0.95\ntg_marginalized_proportions_coefficient <- 0.85\nfafi_marginalized_proportions_value <- 0.75\nfafi_marginalized_proportions_coefficient <- 1\n\n\n\nIntersectionally Marginalized Proportions Score\nWhat proportion of the participants represent and come from multiple and intersecting marginalized identities and communities? This is \\(p_i\\).\nJust like with the Marginalized Proportions Score, you can consider the direct population you are working with, or you can consider your target population. The Intersectionally Marginalized Coefficient, \\(\\beta_i\\), is the same as above. If you consider your target, rather than direct, population, \\(\\beta_i\\) is 0.85. If you are you working directly with the population, \\(\\beta_i\\) is 1.\nFor the Professional Development project, we will again be considering the intersectionality of the students in the school. Nearly all students represent marginalized identities and communities, so \\(p_i\\) is 0.9. The students are one degree away so \\(\\beta_i\\) is 0.85 to dampen the component score. For Family as Faculty as an Infrastructure, just over half of the participants represent marginalized identities and communities, so \\(p_m\\) is 0.75 and \\(\\beta_i\\) is set to 1.\n\n\n\n\n\nCalculate Participant Component Score\nThe Participant Component Score is calculated through the following equation:\n\\[\np_s = p_n \\left( 1 + \\frac{(\\beta_m \\cdot p_m + \\beta_i \\cdot p_i)}{2} \\right)\n\\]\n\n\n\nWe find that the Professional Development Project has a Participant Index (\\(p_i\\)) of 0.81 (adjusted) and a full Participant Score (\\(p_s\\)) of 127. Family as Faculty as an Infrastructure has a Participant Index (\\(p_i\\)) of 0.75 and a Participant Score (\\(p_s\\)) of 88." } ] \ No newline at end of file diff --git a/index.qmd b/index.qmd index d607f58..38b8524 100644 --- a/index.qmd +++ b/index.qmd @@ -655,9 +655,12 @@ Each of these categories and their components are described in some detail below idea, the math, the concepts, are all licensed under a [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/) license[The code itself is licensed similarly under an [MIT License](https://github.com/jeremyfprice/cer-beans/blob/main/LICENSE).]{.aside}, so you are welcome to take these ideas and adapt them to your particular needs and preferences (as long as you give credit where credit is due and share your adapted work alike). -## Participants +## Participants Component Score -People Involved +The Participants Component Score represents the people involved in the project. +Included in this score are the number of people involved, representation of marginalized +community members and identities, and representation of intersectional identities and +community membership. ```{r participants-frame, echo = FALSE} participants_frame <- data.frame( @@ -702,41 +705,90 @@ gt(participants_frame) ### Number of Participants -How many participants involved? This is $p_n$. For this example, there are **6 participants**. +How many participants involved? This is $p_n$. -```{r, echo = FALSE} -number_of_participants <- 7 +For the Professional Development project at one of the elementary schools, there +were 70 directly teachers involved. For Family as Faculty as an Infrastructure, +the participants consisted of the following: + +* 1 School Principal +* 2 Institutional Leaders +* 3 University Faculty and Staff (not part of the project team) +* 4 Neighborhood Participants +* 5 Family Leaders +* 5 Cooperating Teachers +* 30 Undergraduate Students + +This is a total of 50 direct participants. + +```{r, echo = TRUE} tg_number_of_participants <- 70 +fafi_number_of_participants <- 50 +number_of_participants <- 7 ``` ### Marginalized Proportions Score -How many of the participants represent and come from marginalized identities and +What proportion of the participants represent and come from marginalized identities and communities? This is $p_m$. -Also, are you working directly with these marginalized participants, or are you -working with others (such as teachers or health care workers) who are working with -these identities and communities? - -```{r, echo = FALSE} +You can consider the direct population you are working with, *or* you can consider +your *target population* who may be one degree removed when deciding on the $p_m$ +rating. If you consider your target, rather than direct, population, there is a +"penalty" or damping for doing so in the form of a marginalized proportions coefficient, +$\beta_m$. If you are you working directly with the population, $\beta_m$ is 1, +or no damping If you are working with, for example, teachers or health care workers +who will work with your target population, then $\beta_m$ is 0.85 to dampen the +score. + +For the Professional Development project, the students in the school--the actual +target population--predominantly represent marginalized identities and communities, +so $p_m$ is 0.9. The students are one degree away from the teachers, however, +so $\beta_m$ is 0.85 to dampen the component score. For Family as Faculty as an +Infrastructure, just over half of the participants represent marginalized identities +and communities, so $p_m$ is 0.75. We are considering this population directly +(we can consider ripple effects later on) so $\beta_m$ is set to 1 so there is +no damping of the score. + +```{r, echo = TRUE} marginalized_proportions_value <- 1 marginalized_proportions_coefficient <- 1 tg_marginalized_proportions_value <- 0.95 tg_marginalized_proportions_coefficient <- 0.85 +fafi_marginalized_proportions_value <- 0.75 +fafi_marginalized_proportions_coefficient <- 1 ``` -### Intersectionally Marginalized Score +### Intersectionally Marginalized Proportions Score + +What proportion of the participants represent and come from multiple and intersecting +marginalized identities and communities? This is $p_i$. + +Just like with the Marginalized Proportions Score, you can consider the direct +population you are working with, or you can consider your target population. The +`Intersectionally Marginalized Coefficient`, $\beta_i$, is the same as above. +If you consider your target, rather than direct, population, $\beta_i$ is 0.85. +If you are you working directly with the population, $\beta_i$ is 1. + +For the Professional Development project, we will again be considering the intersectionality +of the students in the school. Nearly all students represent marginalized identities +and communities, so $p_i$ is 0.9. The students are one degree away so $\beta_i$ +is 0.85 to dampen the component score. For Family as Faculty as an +Infrastructure, just over half of the participants represent marginalized identities +and communities, so $p_m$ is 0.75 and $\beta_i$ is set to 1. ```{r} intersectionally_marginalized_value <- 1 intersectionally_marginalized_coefficient <- 1 tg_intersectionally_marginalized_value <- 0.95 tg_intersectionally_marginalized_coefficient <- 0.85 -# 0.85 indicates one degree away, e.g., students of teachers, while 1 indicates -# direct engagement +fafi_intersectionally_marginalized_value <- 0.75 +fafi_intersectionally_marginalized_coefficient <- 1 ``` -### Calculate Participation Score +### Calculate Participant Component Score + +The Participant Component Score is calculated through the following equation: $$ p_s = p_n \left( 1 + \frac{(\beta_m \cdot p_m + \beta_i \cdot p_i)}{2} \right) @@ -782,6 +834,25 @@ category_scores <- category_scores |> tg_intersectionally_marginalized_score ) +fafi_marginalized_proportions_score <- adjust_component_score( + fafi_marginalized_proportions_value, + fafi_marginalized_proportions_coefficient +) + +fafi_intersectionally_marginalized_score <- adjust_component_score( + fafi_intersectionally_marginalized_value, + fafi_intersectionally_marginalized_coefficient +) + +category_scores <- category_scores |> + add_category( + "FAFI", + "Participation", + fafi_number_of_participants, + fafi_marginalized_proportions_score, + fafi_intersectionally_marginalized_score + ) + participation_component_list <- c("Number of Participants", "Marginalized Participation Score", "Intersectionally Marginalized Participation Score", @@ -819,6 +890,13 @@ if(intersectionally_marginalized_value < 1) { #participation_table ``` +We find that the Professional Development Project has a Participant Index ($p_i$) +of `r (tg_marginalized_proportions_score + tg_intersectionally_marginalized_score) / 2` +(adjusted) and a full Participant Score ($p_s$) of `r round((1 + ((tg_marginalized_proportions_score + tg_intersectionally_marginalized_score) / 2)) * tg_number_of_participants, digits = 0)`. +Family as Faculty as an Infrastructure has a Participant Index ($p_i$) of +`r (fafi_marginalized_proportions_score + fafi_intersectionally_marginalized_score) / 2` +and a Participant Score ($p_s$) of `r round((1 + ((fafi_marginalized_proportions_score + fafi_intersectionally_marginalized_score) / 2)) * fafi_number_of_participants, digits = 0)`. + ## Engagement Number of contact hours $e_h$. diff --git a/outputs/pdtg_badge.pdf b/outputs/pdtg_badge.pdf index 7cbd94b6e895b835e776d08909e3b8b6da9cd3be..52631eeae418911026553ba3cbc50150f4cd34ea 100644 GIT binary patch delta 30 hcmaFb#qzX^Wr7u}v5BRz$wYf`2%|B2YcwMlD*&5-2&@1A delta 30 hcmaFb#qzX^Wr7u}k%769`9ynh2%|B2YcwMlD*&3=2%-Q0