Gwendolyn MacEwen reading “I Should Have Predicted” – Sir George Williams University, November 18, 1966
Bibliographic record
Abstract
In this Audio of the Week, you are listening to the voice of poet Gwendolyn MacEwen reading in Montreal on November 18, 1966. The reading took place at Sir George Williams University (now Concordia) and it was a joint reading with Phyllis Webb. After an introduction by Roy Kiyooka (an excerpt of which is the first Audio of the Week) Webb reads, followed by MacEwen. Webb jokes that she has “the Toronto plague” (having travelled from Toronto for the reading) and MacEwen too starts her reading by wondering if her voice might “go out” during it and that, if that happens, they can put on the album that they have just recorded for CBC. Thankfully, her voice does not go out, or, as she says, “the voice is intact.” That phrase is the title of an episode about MacEwen on The SpokenWeb Podcast: “The Voice is Intact: Finding Gwendolyn MacEwen in the Archive.” Early on in this episode, producer Hannah McGregor and guest Jen Sookfong Lee listen together to MacEwen reading the poem “The Zoo” from this 1966 recording. As we listen to them listening on the podcast, we hear a gasp and even an exclamation: “Melodious!” What was it in her voice that they were responding to? To try to answer this question through your own experience of listening, this Audio of the Week selects another poem of MacEwen’s in this same 1966 recording: “I Should Have Predicted,” published in The Shadow Maker (1969).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.146 | 0.036 |
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".