Bibliographic record
Abstract
Who are you? Our identities are constantly evolving. They are complex and complicated and beautiful, like the tangled branches of a vine that intertwines. This poem is a critical journey into identity and the historical pieces that produce the whole. It chronicles the uprooting of a people, physically dispossessed and, worst, psychologically traumatized by personas invented by the oppressor to justify their displacement. It tells the tale of uncomfortable truths and personal triumphs, bearing witness to the cognitive dissonance that feeds inhumanity and violence. As painful as it can be, journeying through the darkness to reclaim your truth can be a liberatory step for healing and self-love. With this in mind, below I offer a response to the unrelenting encounters of anti-Black racism, the hypocrisy and hegemony in dominant discourses, the constant self-proving and emotional violence one must navigate as a means of survival, all too familiar with Canada’s unique brand of subtle and polite racism. This poem represents an ancestral journey of personal rebirth, wherein I engage in a rediscovery of identity and the vestiges of various forms of oppression that reside within us, offering an ontological and epistemological journey into the self that provides a comprehensive understanding of who I am.
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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.324 | 0.234 |
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".