Cognitive mapping links human factors to corporate strategies
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
Human factors (HF) can improve business performance. Our objective is to harness individual tacit knowledge from senior directors about human factors as it relates to strategic goals and to make explicit their shared managerial thinking with an aim to identifying improvement opportunities using HF. Individual cognitive maps were drawn during one-hour interviews with seven senior directors of a large electronics firm. The maps were then merged on a common strategic goal of 'improving quality' into a group map containing 221 concepts and 900 loops. In a two hour workshop with the directors, reducing fatigue, improving systems design, and reducing repetitive activities were concepts that emerged as critical-to-quality. Workshop discussions identified untapped improvement opportunities.Directors viewed the maps as a dynamic indicator of their HF performance. Making the connection between HF and strategic goals explicit can help an organisation identify opportunities to improve human, and therefore business, performance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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