HRM in Action: Understanding Impact in HRM Research
Bibliographic record
Abstract
In this symposium, we present a number of international “HRM in Action” Impact Case Studies to demonstrate research relevance and impact. In this initiative, we want to demonstrate impact through the practical implications of HRM research through the presentations of eight Impact Case Studies. We have invited eight research teams who are focused on impactful HRM research to present the key learnings from their cases. This will be followed by a moderated discussion. Collaborating to improve workplace innovation research, practice and policy Patricia Findlay Author: Patricia Findlay; University of Strathclyde The Power of Persistence Author: Madison Khoc; Curtin University - Perth From Disaster to Discovery Author: Peter Jeffrey Holland; Swinburne University of Technology Evaluating a Leadership Development Program through Co-design, and Insider Outsider Action Resear Author: Pradeepa Dahanayake; Facilitating Working Mothers' Returning to Work Transition Using a Work-Family Balance Self-Effic Author: Chiahuei Wu; King's College London 6. Technology-driven interventions in personnel selection: a case study on building effective resear Author: Qian Zhang; University of Ottawa 7. Women in Mining Author: Rana Haq; Laurentian University The living wage in the UK care sector: A simple answer to a complex problem Author: Eva Marianna Herman; Manchester University
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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 | 0.072 | 0.065 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.012 | 0.043 |
| Scholarly communication | 0.026 | 0.033 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".