Bibliographic record
Abstract
On October 27, 2014, Hilde Lindemann presented the John McKendy Memorial Lecture on Narrative at St. Thomas University. The annual lecture, sponsored by the Centre for Interdisciplinary Research on Narrative (CIRN), is named for John McKendy, PhD, a member of the Sociology Department at St. Thomas University and one of the founding members of CIRN, who died tragically in 2008. Dr. Lindemann’s lecture focused on narrative strategies that people in dominant social positions use to counter a counterstory and keep an oppressive social order in place. A counterstory is “a story that is told for the purpose of resisting a socially shared narrative that purports to justify the oppression of a social group ... The socially shared story—master narrative—enters the tissue of stories that constitute the group’s identity, damaging that identity and so constricting group members’ access to the goods on offer in their society.” In her lecture, she explored some of the difficulties that arise when a counterstory sets out to repair that identity, and why the master narratives are so difficult to uproot. Dr. Lindemann has kindly agreed to have the video of her lecture published in Narrative Works.
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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.006 |
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".