Bibliographic record
Abstract
This text was written as a talk for a particular occasion, the Presidential Address on 9 January 2015 during the MLA Annual Convention in Vancouver, British Columbia. I have not removed the traces of this occasion from the text because they are integral to its argument about sites of memory. I hope my readers will imagine themselves as auditors gathered in a large room in the West Building of the Vancouver Convention Centre, built on the edge of a waterway called Burrard Inlet (fig. 1). That waterway, which is represented in several of the images that accompany this text, had — and still has — a different name in the languages of the indigenous peoples who have inhabited the Vancouver area since before it became part of an American hemisphere. Names, in languages that are ancient but also modern, are a key topic in the reflections that follow. I'm grateful to you for the gift of your time. Though my talk explores a view of historical time as a multidirectional and multidimensional phenomenon, I'm aware that our shared time in this room goes in one direction in the simple sense that we'll all be older when this session ends, and probably even more hungry, thirsty, and tired than we are now. I've found that the MLA convention sometimes feels like a memory marathon, with special testings of the brain muscles that allow us to recognize faces and recall the first and last names of acquaintances, and even of good friends, whom we haven't seen for a while. Such experiences of remembering and forgetting contributed to my decision to focus on the MLA itself as one of the two sites of memory I want to explore with you this evening. The other site I want to think about is Vancouver, the place where we are now: a modern city built on a site where humans have been living for the last eight to ten millennia (Carlson 12-16).
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".