Perceived Overqualification: New Directions
Bibliographic record
Abstract
Perceived overqualification, when an employee possesses excess education, experience, or skills, affects a growing number of employees around the world. As such, academics and practitioners alike seek to understand the experiences and outcomes of overqualified workers. This symposium brings together four exciting new research directions for perceived overqualification (POQ) from leading authors. POQ expert, Berrin Erdogan, serves as discussant and provides insights into future research directions for this important topic. Linking Perceived Overqualification to Organizational Citizenship Behavior Author: Yejun Zhang; U. of Texas Rio Grande Valley Author: Margaret A. Shaffer; U. of Oklahoma Author: Kui Yin; U. of Science and Technology Beijing Perceived Overqualification and Work-family Conflict: Examining Pathways Author: Yanan Dong; School of Economics and Management, Beihang U. Author: Aleksandra Luksyte; U. of Western Australia Author: Lin Ma; School of Economics and Management, Beihang U. Author: Jing Jiang; Beijing International Studies U. Perceived Overqualification and Career Success: Is Harmonious or Obsessive Passion Beneficial? Author: Aleksandra Luksyte; U. of Western Australia Author: Joseph Carpini; U. of Western Australia On the Dimensionality of Perceived Overqualification Author: Fran McKee-Ryan; U. of Nevada, Reno Author: Dana Kabat-Farr; Faculty of Management, Dalhousie U. Author: Benjamin M. Walsh; Grand Valley State U. Author: Camilla M. Holmvall; Saint Mary’s U. Author: Remi Labelle-Deraspe; Faculty of Management, Dalhousie U.
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 | 0.030 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.009 | 0.019 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".