A Web of Words: Forging Writer-Researcher Alliances in the Canadian Writing Research Collaboratory
Bibliographic record
Abstract
The history of collaboration in relation to writing is rich and varied; all writing performs as collaboration. The digital context in particular offers opportunities for collaboration in new modes, although digital connotations engender curiosity, resistance and reformulation.The Canadian Writing Research Collaboratory (www.cwrc.ca) builds on that potential in the creation of an online space for research into writing in and about Canada, drawing on the extent to which collaboration and exchange between writers and researchers are built into the fabric of Canadian cultural life. Notwithstanding that symbiosis, certain kinds of exchange are anathema to writers and critics alike. We are torn between the impulse to keep our words our own and a recognition that they emerge from the kind of surfing and sifting endemic to web research, a process and praxis of using. Being inspired by and responding to other people’s words and ideas is the basis for all literature—and literary scholarship. Women’s writing is particularly collaborative, whether we attribute that to gendered permeability of boundaries or to the embattled position of women within a masculinist literary establishment. The CWRC-enabled group of projects on Canada’s women writers aims to produce a rich, multi-faceted, and bilingual trove of insights into that ongoing process of dialogue, response, and repudiation. CWRC offers, then, a precise moment of opportunity, but also the challenge of how to benefit writers as well as scholars, so that collaboration between scholars and writers is supported. Most of all, its goal is to enable a feminist aesthetic and a space for women to speak that at this point competes in the pressure cooker of a digital world still very much a male domain.Moving through Lorraine York, Margaret Atwood, Daphne Marlatt, Betsy Warland and Nicole Brossard via Susanne de Lotbinière-Harwood, this essay explores the dimensions of connection at a time when the digital has opened a wonderfully generative space that nevertheless does not privilege women’s voices or discoveries. It outlines some early CWRC projects, its mentoring potential, and the need to keep more women’s writing and writing about that writing in circulation and preserved for our cultural record. CWRC can enhance readerships, offer a place to sample new Canadian writing, and provide a larger context in which to explore its many alliances, transgressions and betrayals.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".