Bibliographic record
Abstract
The aim of this interpretative study was to find out how higher education administrators thought about emotional competence and what emotional competence ideas and skills they thought were essential for success. Specifically, the aim of this study was to get a better understanding of how administrators in departments of higher education may use their emotional skills to their benefit in their professions. In-depth interviewing takes one step further by focusing in considerable detail on the life experiences and social behavior of selected individual respondents in qualitative research. An in-depth interview was conducted for this qualitative research with eight experienced heads of departments who shared their experiences for a number of reasons. In order to get meaning from the narratives, certain techniques and templates were used. Purposive sampling was used in the 2019-2020 academic year for eight heads of departments at a public university in Izmir, Turkey, with an emphasis on phenomena relevant to the topic and at least four years of administration experience in selecting criteria. The key insight from the study's findings is that participants interpreted emotional competence to entail the ability for university administrators to develop connections by generating trust in order to lead their department. Having an open mind, having an optimistic attitude, being respectful, being inclusive, listening actively were all regarded as key subthemes by higher education administrators. Longitudinal or mixed methods studies, as well as demographic variations in leaders' use of emotional competencies, might be explored in future research.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".