Contingent faculty in ecology and <scp>STEM</scp>: an uneven landscape of challenges for higher education
Bibliographic record
Abstract
Abstract The number of contingent or non‐tenure‐track faculty at colleges and universities in the United States has been growing over the past several decades; they now constitute nearly 70% of the non‐student academic workforce. A significant fraction of contingent faculty teaches in the fields of science, technology, engineering, and mathematics (STEM). As an initiative of the Ecological Society of America (ESA), contingent faculty in ecology were surveyed and the results were compared with a survey of STEM faculty conducted by the Coalition for the Academic Workforce (CAW). Most respondents to the ESA survey were employed in research or research and teaching activities at doctorate‐granting institutions, whereas in the CAW sample, most were engaged in teaching at associate's and master's degree‐granting institutions. The ESA sample was almost evenly divided between women and men; women outnumbered men in the younger age classes, whereas men outnumbered women in the older age classes. The respondents to the CAW survey were older than the ESA respondents, with more men in computer sciences, engineering, and physical sciences, more women in the biological and health sciences, and a balanced gender ratio in mathematics. The ESA survey asked respondents to rank possible activities that ESA could undertake to support contingent faculty. The highest ranked activities included reduced fees for membership, page charges, and meeting registrations, followed closely by small grants for travel and research. The lowest ranked was the formation of an ESA section for contingent faculty. The causes and implications of contingency are analyzed in light of other recent surveys. Academic institutions and professional societies such as the ESA can reduce the loss of qualified individuals from the scientific community by recognizing and legitimizing contingency as an academic career stage and by offering professional development to support the careers of contingent faculty.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".