Perceptions of Intimate Student–Professor Relationships
Bibliographic record
Abstract
Previous research indicates that university students generally disapprove of intimate student–professor relationships (ISPRs), largely due to power imbalances. Further, students may lose faith in their academic abilities and abandon their studies following sexual overture from a professor. The present research used two studies to explore undergraduate student perceptions of ISPRs. Study 1 employed a 2 (Gender of the Initiator: Male or Female) by 2 (Status of the Initiator: Professor or Student) between-groups vignette design. Study 2 employed the same design as Study 1, adding a third variable—the quality of a reference letter (Overly Positive or Negative) provided by the professor for the student in the relationship. Both studies included sexism as a covariate. Participants were asked to rate four dependent variables: the degree of sexual harassment, power imbalance, impacts on others, and the appropriateness of consequences. In Study 1, participants in the Professor as Initiator conditions rated sexual harassment, power imbalance, and impacts to others more severely, regardless of gender. In Study 2, participants in the Overly Positive Letter conditions rated the power imbalance more severely, regardless of gender or status. Adding the reference letter component in Study 2 resulted in participants rating all four dependent variables more severely than those in Study 1, as predicted.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".