The isolation of online adjunct faculty and its impact on their performance
Bibliographic record
Abstract
Using a grounded theory qualitative research approach, this article examines the experiences of 28 adjunct faculty members who work at the same university, exploring their views on whether periodically meeting face-to-face with management and peers has the potential to affect their motivation on the job and consequently the quality of education they provide to students. A few management representatives also shared their perspectives on the phenomenon; this enabled the researcher to compare the views of these two populations on whether face-to-face contact among faculty enhances teaching performance. The results of this study suggest a few issues that online schools must address in their efforts to improve adjuncts’ sense of affiliation and loyalty to their institution, which in turn will positively affect student retention levels. The main issues of concern to adjunct faculty are (a) inadequate frequency and depth of communication, regardless of the means used, whether online or face-to-face; (b) lack of recognition of instructors’ value to the institution; and (c) lack of opportunities for skill development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".