The objeclil'e of Ilris paper is to onol)':e homeownership rates for
Bibliographic record
Abstract
Aboriginals and whiles. bolh o/whom are Canadian citizens. Da/U wcre obtained from Thc PI/blic Use Microdata Files for Individools (PUMFI) drownfrom the 1996 CensllS prol'ided by StalUI/CS Canada. The imptJct of race is cumined lISing logistic regre.tsI0n models and controlfing for socioeconomIc and demographrc churacteruties of the Aboriginal und white populution a/Toronto. CMA. Results reveal that race is a barrier 10 Aboriginal homeuwnership el'en wllel! Aboriginals hal'c tIre same socioeconomic olld demographiC charue-terjJ'tics as.... hites. Thejindings sl/ggest that f urtller study is needed 10 determine the ctentto which discrimination in housing might be a factor. L 'objectij de eet ar,iclc cst d 'anal)'ser fes,al/.r d 'acquisition de proprietes pour les arllochlones et les bJanes. qui sont IOUS deu.f citoyens calladiens. Les dO/lllees on ' ete obtenues u partir des jichiers de microdonnees d'l/sage public des persomres intiil'itiuclles, tIres du recensement de 1996fol/rni por Statistique Cunada. L 'impact de la race est camine en utifisunl des modeles de regression logistiques el en contr6101/1 les caracteriSliques socio-eeonomiques e t demographiqlles de fa poplliation des autochlOnes et des bJanes u Torollto. dons fe recensement de 10::one metrvpolrraine. Les resulrau rel-elent que fa race e~·t Ull obstacle u I 'acqllisitlOn de propriete.f par les ml/ochlolles meme si les Al/tochlOlles possedenl les memes caraclerisliques socio-ecollom;qlles el demograp/r iqlles que les bJancs. Les resultals s llggimml d 'effecruer dal'onwge d 'etudes pour delerminer {'item/lie selon loquelle 10 discriminution en moliere de {ogement pew eire Iln f acl#!Ur.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.228 | 0.068 |
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".