Storytelling for Social Justice: Reconceptualizing Invisibility, Legitimacy, and Self-Censorship via <i>Stories of Failure</i>
Bibliographic record
Abstract
Building upon scholarship on stories – including oral-history and oral-tradition – this article considers often-silenced narratives in the form of stories of failures. Comparison between empirical research and qualitative methodologies highlight value of fictional storytelling, which has been mobilized by researchers to protect the anonymity of vulnerable groups and the intersectionally marginalized in research. While hierarchies in power lead to self-censorship and self-delegitimization as observed by bell hooks and Paulo Freire, stories of failure may resist social pressures to present successes – which in themselves have led to widely disseminated cases of academic fraud. Additionally, stories of failures may help acknowledge Kemmis and Mezirow’s considerations of negative emotions as valid, toward resolution, resistance, and social change. Stories may help elucidate the hidden curriculum, which confounds efforts toward social justice under Rawlsian theories. Lastly, these stories may elucidate the impacts of interest convergence under critical race theory, towards the promotion of social justice intersectionally.
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 | 0.023 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.013 | 0.070 |
| Scholarly communication | 0.020 | 0.027 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".