Repositioning Organizational Failure Through Active Acceptance
Bibliographic record
Abstract
This paper considers the way organizations respond to failure by actively repositioning the failed outcome as success. When an organization fails to meet planned goals, they do not necessarily learn from the experience, automatically terminate the plan, or persist with the failing course of action. Instead, another response is to shift original aspirations by recasting what was achieved, acting as if the ensuing failure is positive, despite indicators suggesting otherwise. As a mode of organizational interpretation, this repositioning reformats the criteria for what is success in order to move forward, enabling organizations to continue failed outcomes and their tasks that are well past their use-by date. After detailing this adjustment, we model an active-acceptance protocol on failure, discussing whether organizational effectiveness is predictable from how firms respond to failure in this way. The paper fills a gap in dialogue specific to failing by opening an alternative path to understand how organizations frame failure differently.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Organization theory paper on how organizations recast failure as success; the object is organizational behavior, not research.
It studies how organizations reinterpret failure, not how research is conducted or evaluated.
Organization theory on reframing organizational failure as success; not research failure or research systems.
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 | 0.023 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".