Bibliographic record
Abstract
Each generation’s obituaries reflect social views on gender due to how representations of the deceased and the social norms informing those representations are mutually reinforcing. Currently, representations of gender in obituaries are not equitable; greater focus on gender-equitable discourse is necessary to correct the discrimination. My study looked at the state of current obituaries to answer the question: How can newspaper obituaries be written to promote gender equality? The research took a qualitative critical discourse analysis approach that involved speaking with funeral directors in Victoria, B.C., Canada and reviewing 350 obituaries from April and May 2016 published in the Times Colonist newspaper (via legacy.com). Applying van Leeuwen’s (2008) social action network model to guide analysis of the ten longest obituaries, I recontextualized the obituary discourse to illuminate its constitutive social practice. My findings indicate that the gender gap in obituary writing is closing, but pronounced differences in obituary language used to describe men and women remain. \nKeywords: gender equality; death discourse; obituaries; social practice; language; critical discourse analysis
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 | 0.015 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".