A new hope: A holistic framework for understanding workplace experience
Bibliographic record
Abstract
This paper introduces a novel conceptual framework to holistically consider workplace and its intrinsic organisational value. The framework recognises ‘workplace’ as a polyseme, a single word that has multiple associated meanings, and embraces (rather than refutes) its plural spatial, technological and cultural interpretations, alongside interrelated business impacts. To substantiate the framework, this paper reprises and synthesises seminal workplace-related models, including Trist and Bamforth’s ‘sociotechnical systems’ and Becker and Steele’s ‘workplace ecosystem’, among others. Consequently, this paper explores how the framework provides a new opportunity to consider ‘workplace experience’, and offers an ontology to substantively understand, evaluate and potentially even benchmark workplace experience holistically, for diverse and distributed organisational workplaces, making its utility both work location and sector agnostic. The framework therefore not only broadens the scope for workplace insights and decision making, but it also offers a basis from which to critique other assertions or claims of truth about workplace and workplace experience. While the immediate audience for this paper — readers of this journal — will most likely be invested in physical workplace elements first and foremost, the framework promotes collaborative opportunities through wider appreciation and understanding of different workplace perspectives, as well as their interconnectedness and interdependencies. The paper concludes with suggested opportunities and further areas for research and development.
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.026 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".