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Record W2227600649 · doi:10.1609/icwsm.v7i1.14413

Understanding the Roles and Uses of Web Tutorials

2021· article· en· W2227600649 on OpenAlex
Ben Lafreniere, Andrea Bunt, Matthew Lount, Michael Terry

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueProceedings of the International AAAI Conference on Web and Social Media · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWikis in Education and Collaboration
Canadian institutionsUniversity of ManitobaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceVariety (cybernetics)DebuggingTask (project management)World Wide WebRepertoireWeb applicationShadow (psychology)Human–computer interactionData scienceMultimediaPsychologyArtificial intelligence

Abstract

fetched live from OpenAlex

In this paper we identify roles and uses of web-based tutorials through an examination of tutorials’ comments sections. Through this analytical lens, we find that web tutorials serve a variety of needs, providing: in-task help for users with an immediate, specific goal to accomplish; a means for users to proactively expand their repertoire of skills; and an opportunity for novices to shadow and experience an expert’s work practices. We also find a number of emergent practices in tutorial comments. Users post “help-me” stack traces, a type of comment useful for debugging tutorial content; use comments sections as opportunistic support forums; and turn to comments sections for social and technical validation of their personal skill sets. Collectively, these findings enrich existing perspectives on web-based tutorials and argue for new mechanisms to support these various use cases.

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.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.595
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.111
GPT teacher head0.326
Teacher spread0.215 · 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