Bibliographic record
Abstract
For me - an Afro-L’nu interdisciplinary doctorate candidate rooted in cinema and media studies - vampirism resembles cinematic realism: a visuality of authenticity effected in scene, setting, and storyline on-location respective to narrative milieux. However, what often defines reality as opposed to preferred realism is that Black positionalities continue to be afflicted by disparity, exploitation, exclusion, and inaccessibility alongside systematic anti-Blackness which extenuate our adversities. This contrasts with the avid albeit ambiguous initiative of equity, diversity, inclusion, and accessibility (EDIA) sweeping through academic spaces in the wake of reconciliation campaigns. Moreover, this initiative is vampiric in its avid solicitation of efforts and insights from the very marginalized positionalities it purports to uplift. Too little, if anything. Too late, if ever. The wealth of lip service paid in comparison to what pittances we marginalized peoples are afforded. I find myself immortalized by pearls of wisdom which speak to ancestral strength and blood memory, akin to how kernels from an artifact transform Dr. Hess Green and Ganja Meda into the vampiric undead. This personal essay offers a discourse analysis of Ganja & Hess (1973) that incorporates my own positionality and academic exegesis, notably revelations as to what vampiric contingency underlays my transformation, survival, and eventual demise.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.006 |
| Insufficient payload (model declined to judge) | 0.078 | 0.031 |
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".