The Dark side of helping: Escalation of commitment
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
In the real world, employees may be presented with difficult tasks that could be tackled in multiple ways and with available resources. On top of this, with deadlines, few external resources, and other tasks that employees typically face, thinking tends to be narrowed and so do the actions that follow. This could lead to a persistent course of action that leads to failure. We call this situation escalation of commitment. When our coworkers offer help and we are stuck and have invested time and effort into near-impossible tasks, is it worth accepting this offer of help? Or, would we rather risk more time and resources and instead persist in solving this near impossible problem? In the latter option, the individual may experience burnout and stress. For the organization, deadlines would not be met, and objectives could not be accomplished. My research looks at these helping behaviours and whether they lead others astray in an escalation of commitment. Specifically, I predict that individuals who have invested in a failing course of action are less likely to abandon this path when they receive help from others. This intersection of escalation and helping behaviours are important because when employees attempt to help a coworker who is invested in an extremely difficult task, they may be doing more harm than good.
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 | 0.001 | 0.001 |
| 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 | 0.000 | 0.001 |
| 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