Bibliographic record
Abstract
This paper advances what we call Memory-Witnessing as Methodology as a valid methodological approach to research. Rooted in anti-colonial and decolonising perspectives, this methodological approach challenges and subverts Eurocentric systems of research. It intentionally and necessarily resists traditional data gathering practices and interrupts colonial knowledge systems that seek to discipline, assimilate and often cause greater emotional labour for Indigenous, racialised and marginalised bodies and their decolonising efforts. By disrupting Eurocentric methodologies, apologies and ethics, Memory-Witnessing centres the voices, resistance and agency of the colonised. By employing embodied research, it ensures the marginalised have a safe collective, and intersectional voice to expose systemic oppression within their institutions. This methodological approach also demands accountable listening to all that orality, silence, silencing and colonial amnesia entail. As a relational methodology, Memory-Witnessing aims to decolonise higher education into a more equitable, representative and epistemologically just place for those who have been historically, persistently and socially marginalised.
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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 | 0.070 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.049 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".