Improving team performance using repertory grids
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose This paper seeks to explore how repertory grids can be used to address IT team performance issues. The technique is introduced along with the process of creating and analyzing repertory grid data. Design/methodology/approach To explore the application of the repertory grid technique to team performance issues. An example focused on eliciting the essential soft skills needed by programmers to effectively interact with IT team members is illustrated. Research limitations/implications To researchers, the main benefit of this paper is that it introduces a technique that is easy to use, enables the researcher to easily determine the relationship between constructs, is free from researcher bias, and can be applied to a wide variety of team‐related research studies. Practical implications This research presents a means by which human resource managers, hiring personnel, and team leaders can easily determine essential skills needed on the IT teams of the organization, thereby deriving a “wish list” from key IT groups as to the desired non‐technical characteFristics of potential new team members. Originality/value Shows how repertory grids can be used to address IT team performance issues.
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.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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 it