Exploring Dehumanization and Humanization in Organizational Contexts
Bibliographic record
Abstract
Humanization and dehumanization broadly refer to the attribution or denial of ‘humanness’ to others: traits that make up the ‘human essence’ or make humans unique from other animals. There has been a recent surge of psychological research demonstrating the importance of humanization and dehumanization across many phenomena. However, (de)humanization is typically entwined with a variety of related constructs (e.g., mind perception, objectification), leading to a lack of conceptual clarity. Additionally, despite the close attention paid to dehumanization and humanization in the psychological sciences, the extension of these concepts to organizational scholarship has been limited. Some of the questions we will discuss include: How should scholars define humanization and dehumanization, and how do we distinguish these constructs from mind perception, objectification, and anthropomorphism? Which organizational contexts/processes are most likely to motivate people to (de)humanize others, or even themselves? What are some barriers to the application of (de)humanization to organizational research?
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.021 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".