Bibliographic record
Abstract
When is an Ally not an Ally to Indigenous, marginalized, or racialized peoples? How important is it that today's youth are stepping up to fight the good fight for others? Many questions arise when mentioning allyship and how it works. What is the benefit? Is there a benefit? All good questions, and in this article, I intend to address the real issues involving the support of others towards these racialized and marginalized peoples. It is noted that not all “famous” people, or celebrities that support our causes have the best intentions, and they may take away from those on the ground floor doing the work. It is tireless, unappreciated work that goes unrecognized almost all the time, with the primary individuals or groups being placed in the spotlight…or even the limelight. This is about supporting those people as human beings without asking for acknowledgment or accolades and feeling fulfilled by their actions only. Exposing those such as Lorde, the artist from New Zealand, or educators from across this country whom we call pretendians builds an alliance with those who want social justice and believe that we all fight the good fight.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.122 | 0.032 |
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".