Relative deprivation, self-interest and social justice: why I do research on in-equality
Bibliographic record
Abstract
Purpose – The purpose of this paper is to offer an insight into why men do research on in-equality. Design/methodology/approach – The author utilizes autoethnography, as a form of self-reflection, to help make sense of the own experiences and to connect it with the broader world. It is a narrative based on personal experiences which connects the author's biography with his research endeavours. It also enables to engage in self-analysis and self-awareness of the motives for conducting research on in-equality. Findings – In this narrative, the author shares his journey as an equality scholar, and how his multiple identities as a visible minority, an immigrant to Canada, and a gay person shapes my worldview, attitudes, and beliefs, which in turn influences his own work on equality and diversity. The narrative is based on the intersection of multiple identities, and not just solely based on the author's gender. The attribute feeling deprived on behalf of others, rational self-interest, and social justice as the chief reasons for engaging in in-equality research. Research limitations/implications – Autoethnography is inherently subjective, based upon the author's own biases and interpretation of events, but the subjectivity can also be an opportunity for intentional self-awareness and reflexivity. Given the multiple identities that the author holds, some of the experiences recounted here may be unique to the author, and some may be shared with others. Thus, it is not the author's intention to represent, in general, why men do in-equality research. Originality/value – This autoethnography has allowed the author the opportunity to be self-aware of the complexity of the multiple identities. This self-awareness also allows the author to be more respectful, authentic, and inclusive of others. The author hopes that these reflections will resonate with some of you, and perhaps inspire one to engage in similar work, for reasons that are unique to one and all.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".