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Record W2328671840 · doi:10.16995/dscn.28

Asymmetric Digital Collaboration and Collective Authorship: On Digital Genres and Writing Processes for <em>CanLit Guides</em>

2016· article· en· W2328671840 on OpenAlexvenueaboutno aff
Mike Borkent, Jamie Paris

Bibliographic record

VenueDigital Studies / Le champ numérique · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicDigital Humanities and Scholarship
Canadian institutionsnot available
Fundersnot available
KeywordsCollaborative writingTeamworkLibrary scienceSociologyCollection developmentWorkflowDigital libraryProcess (computing)Writing processDemocracyPolitical scienceComputer sciencePedagogyArtLiterature

Abstract

fetched live from OpenAlex

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 (<em id="d1e65" class="term">CanLit Guides</em>) 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.231
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0080.006
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.047
GPT teacher head0.265
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes2
Has abstractyes

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