To Keep Close or to Let Loose: Recipe for Sustainable Quality Dyad
Bibliographic record
Abstract
This study’s objective was to determine factors that that sustain a quality dyad. Leaders are known to consciously and sub consciously form two groups; in-group and outgroup members. Studies have revealed that in-group members work overtime and perform extra duties and in turn, get favours from the leaders including career mobility and access to information, among other favours. Literature is unclear on how these groups are formed and this paper embarked on finding out the recipe of the formation and sustainability of a quality dyad. It was hypothesised that being a male member, trust and competence are not recipes of a high-quality relationship. Descriptive survey was employed; a population of 19 leaders were responding to questions about their 169 employees who report to them directly. Primary data was collected using semi-structured questionnaires.122 pairs of leaders and their direct reports was the response rate (72.2%). Descriptive statistics were used to analyse the data. The hypothesis was tested using logistical regression technique. The results showed that competence and trust are the recipe for an inclusion into the in-group of a leader. Gender, on the other hand, was not a recipe for a sustainable quality dyadic relationship. It is recommended that employees should ensure high level trustworthiness and competence for them to be kept close by the leader. The paper suggests that more variables can be considered as recipes for the quality dyadic relationship. These findings add significant value on both theory, policy and practice.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 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".