Sustainability, organizational learning, and lessons learned from aviation
Bibliographic record
Abstract
Purpose While the importance of organizational learning for sustainability has been stressed by a number of authors in the literature, the practicalities of how organizational leaders might foster such learning are seldom treated. This paper seeks to demonstrate that there is much that could be learned from the aviation industry about organizational learning practice that could be gainfully applied by organizations in attempting to address the demands of triple bottom line sustainability. Design/methodology/approach The exemplary safety record of the US commercial aviation industry is explored in this paper, and the principal functions of its underlying learning and adaptive system are reviewed. Generalized application of such a learning and adaptive system in an organization operating according to triple bottom line sustainability principles is described. Findings Through the interaction of various functional components described in the paper, the commercial aviation industry has created a learning and adaptation support system that has significantly and effectively increased air travel safety. The characteristics of such a learning and adaptive system can be employed by any organization to vastly improve its performance as it pursues triple bottom line sustainability. Originality/value The learning and adaptive system approach presented expands the steps of understanding, creating and delivering triple bottom line sustainability by changing internal processes, organizational learning, and employee mindsets.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".