Introduction: Organizational Wrongdoing as the “Foundational” Grand Challenge: Consequences and Impact
Bibliographic record
Abstract
Abstract Organizational wrongdoing is still very much prevalent in today’s society. Traditional and social media are full of examples of organizations engaging in unethical or illegal behavior. While it is difficult – if not impossible – to establish whether the ever-increasing number of reported cases of wrongdoing is due to an actual increase in the phenomenon (objectivist view of wrongdoing) or to more attention being paid to it (social-constructivist view of wrongdoing), the fact remains that organizational wrongdoing seems to have become the norm rather than an exception in our everyday life. This is concerning, as organizational wrongdoing tends to undermine trust in fundamental institutions, such as the Market, the State, Religion, and Law, and may lead to them being replaced by other – sometimes less desirable – institutions or create an “institutional void.” Because of its potential impact on established institutions, organizational wrongdoing deserves to be closely monitored and further examined. This volume of Research in the Sociology of Organizations is an attempt to draw attention to the theoretical and empirical relevance of the topic, consolidate and extend the knowledge accumulated in this area of research, and highlight potential direction for future research. The volume focuses in particular on the variegated consequences and impact of organizational wrongdoing.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".