EXCLUSION IN ACADEMIA: LATINA FACULTY STRUGGLE TOWARDS TENUR
Bibliographic record
Abstract
The purpose of this qualitative case study was to explore the lived experiences of underrepresented tenured Latina faculty in one four-year university in the southeast area of the United States to identify barriers towards achieving tenure.Eight tenured Latina faculty with experience of 7 to 20 or more years in a tenured position provided their perceptions and experiences of the challenges and support they encountered in their pursuit of tenure.A snowball sampling technique produced eight participants from an initial recruitment from an online search.Semi-structured interviews via in-person and audio-video conferences offered rich descriptions of the Latina faculty's experiences for coding and analysis.The NVivo for Mac software (QSR International, 2015) supported the coding and analysis process of the participant's responses.Five main themes emerged from the patterns found in the analysis.The five findings included: organizational exclusionary practices against Latina faculty at the university; white male-oriented culture where resources are used to benefit white males; demoralizing micro-aggressions towards Latina faculty from white faculty; the university leadership's lack of action and accountability to address diversity and inclusion challenges; and the lack of support networks and mentoring to help guide Latina faculty.These findings described an exclusionary academic environment, where the Latina faculty often felt insulted, isolated, and underappreciated with little to no opportunity to advance or contribute equally to the university.This study contributed to the literature by addressing various reasons higher educational institutions need to remove barriers that negatively affect Latina faculty seeking tenure actively.
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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 | 0.016 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.043 | 0.025 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".