Bibliographic record
Abstract
There is considerable anecdotal evidence that families with children, particularly single parent families, face prejudice from landlords when attempting to rent apartments. The same applies to lower socio-economic class families versus those with higher socio-economic status. This research attempts to measure the extent to which this prejudice exists. Using the Craigslist site for Toronto, Canada, two e-mails were sent to each landlord offering a unit for rent to measure reaction to two different types of tenant households. The various categories of households are shown in the chart below. Socio-Economic Status: High Low Household type: Couple Single Parent w/ Kids Married Couple w/ Kids Whether a response is received and the time to respond is recorded for each e-mail sent. As well, the tone and content of each e-mail is analyzed. For example, a landlord could be sent an e-mail from a couple and a couple with children to examine differences in response to families with children. Alternatively the landlord might be sent an e-mail from a high socio-economic couple and a low socio economic couple to examine differences in whether and how long it takes to respond. Socio economic class was indicated through grammar, spelling and amount of information provided in the e-mail. The percentage of responses received by each group and the response time difference measures whether landlords respond differently to one group or another. Content of the e-mails will also be analyzed.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".