Board 140: Towards Servingness-Oriented Mentorship
Bibliographic record
Abstract
Abstract Hispanic-Serving Institutions (HSIs) present opportunities for social mobility of marginalized students in STEM. In the USA, Hispanic-Serving status is defined by enrollment of least 25 percent Hispanic students. As such, HSIs dedicated to increasing Hispanic representation in STEM would best serve their mission by preparing their students for longitudinal success, by focusing on educational opportunities beyond short-term retention. In other words, HSIs would best earn their status by moving beyond enrollment into true servingness. In pursuit of servingness, a program has been developed at an HSI which is designed to prepare students for success in postgraduate studies or the workforce while equipping them with skills related to self-directed learning. In this program, participating students are financially supported as they pursue industry-valued certifications, entrepreneurship training, and design project experiences. Students explore these opportunities under the guidance of industry mentors. The dynamics of mentorship have been well-explored in available literature, but there is a lack of characterization of servingness within a mentorship paradigm. To explore the mentorship process through a critical servingness lens, efforts were made to assess and understand mentors' predispositions to the mentoring process. To accomplish this, we interviewed mentors before their participation in a mentoring training workshop. We interviewed 5 industry mentors using a semi structure qualitative interview focused on their experiences with mentoring, and ideas on how to support marginalized students. Data analysis was performed through reflexive thematic analysis via inductive and deductive coding. Deductive coding follows the recommendations/findings of the National Academies of Sciences, Engineering, and Medicine on effective mentoring in STEMM. Preliminary results indicate that because students are expected to be self-directed in the project, they should also be self-directed in the mentor-mentee relationship. This framing overestimates student's cultural capital within a marginalized experience. Moreover, mentors acknowledge the effects of systemic racism and other forms of oppression on the experiences of their mentees, but frequently feel ill-equipped to help students navigate that oppression. As we characterize the mentors' approach to and conception of mentorship before exposure to the workshop, we reveal opportunities to build structures of servingness within the mentoring training process.
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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 | 0.050 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.012 | 0.007 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".