Inclusively Studying Inclusion: Centering Three Modes of Student Partnership in Assessing Equity and Inclusion in an Academic Department
Bibliographic record
Abstract
In the 2019-20 academic year, two fourth-year students (Nicole and Loops) partnered with a professor (Ben) to explore questions of inclusion, equity, and diversity within the Haverford College psychology department.Our goal was to translate what we felt we knew from our lived experiences as students-that our academic experiences were not equitableinto quantitative and qualitative data to drive conversations of equity and inclusion forward with faculty in the department and the institution overall.The project culminated early in the pandemic in May 2020, but we continued to meet virtually-nearly weekly at times-beyond then.Going into the 2020-21 academic year, these conversations became about the relevance of our project for the conversations about diversity, inclusion, and antiracism that had intensified at Haverford College, culminating in a student strike that fall.Two questions guided our reflective discussions: what did we accomplish and, perhaps more importantly, how did we accomplish it?This latter question led to our current reflection.Our cooperative efforts were rooted in partnership at every level.We came together as student-faculty partners, our exploration was grounded in discussions with fellow studentsstudent-student partnerships-and our work was leveraged in collaborations with the institution-student-institution partnership.In this reflection, we describe how redefining student partnerships was central in developing and implementing a survey to assess inclusion, equity, and diversity within an academic department at a small liberal arts college.As we continued to meet to discuss our project and the current situation at Haverford and beyond, we wanted to narrate our work to better understand the process and demonstrate the possibilities of conducting research using a partnership model.When we started writing this reflective essay, we did not have a particular starting point, especially as we were pushed out of our comfort zones with writing centered around our lived experiences rather than the data collected.We each drafted preliminary ideas which developed into a brief abstract where we first detailed the idea of the three forms of partnership.We continued writing independently in response to reflective essay prompts and continued coming together to discuss questions and next steps, eventually bringing us to a cohesive essay.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.038 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.011 | 0.014 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.003 | 0.039 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".