Lineages, Geographies: A Review of the Canadian Festival of Spoken Word, Toronto, 11-14 October 2006
Bibliographic record
Abstract
Veteran spoken-word performer and director of the Calgary International Spoken Word Festival, Sheri-D Wilson kicked off the opening ceremonies of the 2006 Canadian Festival of Spoken Word in Toronto, a fitting gesture, given that Calgary is the newest team addition to the national event. In the midst of entertaining poetry featuring drag queens in pasties, Wilson lamented having recently heard a younger poet “pissing on the Beats.” She implored an audience of poets, “We must as a group know our history.” Veteran dub poet activist Lillian Allen echoed the importance of history when she performed as the Female Honouree on closing night: “if you don’t know where you’re coming from, you can’t see where you’re going to.” The Festival proved that this “we” derives from an eclectic mix of poetic lineages, histories and networks within and beyond poetry. For example, references to Apollinaire, the nineteenth-century-born French-speaking Polish—Swiss—Italian who coined the term “surrealist,” shared the stage with hip-hop legend Tupac Shakur, who coined the much misunderstood acronym “THUG LIFE” (The Hate You Give Little Infants F — -s Everybody).
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.020 | 0.044 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".