DIFFERENCES IN CONFLICT MANAGEMENT BASED ON GENDER AND PERSONALITY TYPE
Bibliographic record
Abstract
This study examines factors influencing studentconflict management styles in a team-based second yearmechanical engineering design course. Maddux describedconflict management along the dimensions ofassertiveness (seeking to meet one’s own needs) andcooperativeness (seeking to meet the other party’s needs).The key research questions in this study were how conflictmanagement styles changed as a result of participation inan intense team-based course and whether gender orpersonality type influenced students’ conflict managementstyles. Students completed a pre-course team formationsurvey that included prompts on how they would deal withdifferent scenarios representing common team conflicts;students responded to the same prompts again in aproject exit survey. Students’ responses in these surveyswere used to code their preferred approach for dealingwith conflicts. Two independent reviewers worked fromrandomized, anonymous survey data and coded students’responses along the two dimensions of Maddux’ model.The results indicate conflict management style iscontext dependent (the distribution of responses changedfor the different survey prompts). The most commonlyused conflict management style was Compromising, inwhich parties find a middle ground but neither fullyachieves their goals. A statistically significant reductionin assertiveness was found between pre- and post-surveys.Statistically significant differences in assertiveness werealso noted with a number of Myers-Briggs personalitytype pairs in the pre-survey. The fact that similardifferences were not observed in the post-survey suggeststhat the project experience has a normalizing effect onconflict management style. Meaningful statisticallysignificant differences in conflict management style basedon gender were not observed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".