Asymmetric Digital Collaboration and Collective Authorship: On Digital Genres and Writing Processes for <em>CanLit Guides</em>
Bibliographic record
Abstract
This paper discusses the unique asymmetric collaboration process used at CanLit Guides in the first phase of its development. CanLit Guides began as a project to mobilize the massive digital archive (1959-2008) of the scholarly journal Canadian Literature. The Guides introduce undergraduate students to areas of scholarly and critical concern in the larger field of Canadian Literature and culture. The editors of Canadian Literature enabled graduate students to develop teamwork, research, teaching, and digital writing skills by employing them as developers, researchers, and writers. The project supports open access, scholarly collaboration, and the creation of new digital genres. As the project evolved, however, it became clear that getting a team of scholars to work on a hierarchized, or what we call "asymmetric," collaboration between the editors and the graduate students, is particularly difficult, and can lead to issues of doneness and sprawl. Producing a collaborative and democratic workflow process enabled us to write a robust collection of guides in innovative digital genres. This paper pays particular attention to issues of authorship that come up with any collaborative digital writing project, and it discusses the complexities of the graduate student experience of working on a digital pedagogical development team. Cet article discute du processus unique de collaboration asymétrique qui a été utilisé dans les guides sur la littérature canadienne (CanLit Guides) au cours du premier stade de leur développement. Les guides CanLit ont débuté comme projet visant à mobiliser les archives numériques massives (1959-2008) du journal érudit de la littérature canadienne. Les guides initient les étudiants de premier cycle à des domaines de préoccupation universitaire et critique dans le champ plus large de la littérature et de la culture canadienne. Les rédacteurs de Littérature canadienne ont permis aux étudiants diplômés de développer leurs aptitudes de travail en équipe, de recherche, d'enseignement et de rédaction numérique, en les employant comme développeurs, chercheurs et rédacteurs. Le projet soutient l'accès ouvert, la collaboration universitaire et la création de nouveaux genres numériques. Cependant, à mesure que le projet a évolué, il est devenu clair qu'il était particulièrement difficile d'obtenir d'une équipe d'universitaires qu'ils travaillent en collaboration hiérarchisée, ou ce que nous appelons asymétrique, entre les rédacteurs et les étudiants diplômés, et que cela peut mener à des problèmes de finesse et d'étalement. La production d'un processus de flux de travail collaboratif et démocratique nous a permis de rédiger une collection robuste de guides dans des genres numériques innovateurs. Cet article porte une attention particulière aux problèmes de la paternité des œuvres qui surgissent lors de tout projet de rédaction numérique en collaboration, et discute des complexités de l'expérience des étudiants diplômés à travailler au sein d'une équipe de développement pédagogique numérique.
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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 | 0.019 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.019 | 0.026 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".