What we can learn from de-valued and marginalised work/research
Bibliographic record
Abstract
Purpose – The purpose of this paper is to reflect on how QROM has become an outlet that gives voice to de-valued and marginalised work/research and those who undertake it. The authors present an overview of the research published in the journal over the past ten years that has provided rich accounts of hidden and marginalised groups and experiences. The authors also summarise the unique contributions of the research covered in the special issue the authors co-edited on doing dirty research using qualitative methodologies: lesson from stigmatized occupations (volume 9, issue 3). Design/methodology/approach – The authors adopt a literature review approach identifying key pieces covered in QROM that surface various forms of qualitative methods employed to illuminate the everyday practices of “Other” occupations, individuals and groups; experiences situated outside of the mainstream and often hidden, devalued and stigmatised as a result. Findings – The authors conclude that the articles published in QROM have demonstrated that in-context understandings are critically important. Such studies offer insights that are both unique and transferable to other settings. A number of invisible or hidden issues come to light in studying marginalised work/ers such as: the hidden texts, ambiguities and ambivalence which mark the experiences of those marginalised; that stigmatised work/research is embodied, emotional and reflexive; and, that expectations of reciprocity and insider-outsider complexities make the research experience rich, but sometimes uncomfortable. Originality/value – The authors review the research published in QROM over the past ten years that contributes to understandings of work, research and experiences of those who are often de-valued, silenced and marginalised in mainstream business and management studies.
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 | 0.081 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.012 | 0.064 |
| Scholarly communication | 0.042 | 0.060 |
| Open science | 0.005 | 0.024 |
| Research integrity | 0.012 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 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".