Resilience and Post-Traumatic Growth after Discriminatory Job Loss: The Case of Academics Dismissed after Turkey’s 2016 Coup
Bibliographic record
Abstract
This study is about the impact of discriminatory job loss (DJL) on individual attitudes. It is based on interviews with 36 academics who were inequitably and involuntarily fired, and aggressively and punitively discriminated against. We extend previous research on workplace discrimination by exploring the effects of discriminatory job loss on a skilled workforce and by going beyond the job loss itself to examine coping mechanisms, resilience and post-traumatic growth. We found that gratitude, patience and optimism or pessimism about one’s future and career were leading individual factors in the ability to cope with discriminatory job loss. Such coping mechanisms, and their roles in resilience and post-traumatic growth, were described to us by academics in Turkey and abroad. Summary This study of DJL (discriminatory job loss) is a contribution to the literature on job loss and workplace discrimination. In particular, we aim to improve understanding of the psychological outcomes of job loss and termination while exploring their specific causes. Unlike previous studies, this one shows a hidden, unknown and veiled side of DJL, as changes in attitudes are hard to notice, or in some cases unnoticeable, until individuals act or speak out. We extend previous workplace discrimination research by exploring the effects of discriminatory job loss on skilled workers and by providing a broader perspective that includes positive aspects, such as resilience and post-traumatic growth. We found that gratitude, patience and optimism or pessimism about one’s future and career were leading determinants of the ability to cope with discriminatory job loss. Among academic victims of DJL, the ability to cope was key to resilience and strategies for post-traumatic growth. Thus, unfair firing and punitive discrimination do not necessarily stop highly skilled workers from having hopes, expectations and plans for the future. They struggle to reduce external negative outcomes by combining resilience and PTG strategies with internal attitudes of optimism, gratitude and patience. On a practical note, workplace discrimination may be prevented through resilience and PTG strategies.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".