Comic Vine: Participatory and Idiosyncratic Documentation of a Semantic Platform
Bibliographic record
Abstract
Comic Vine (CV) is a semantic platform focused on documenting published comics. Developers Dave Snider, Ethan Lance, and Tony Guerrero launched the site on December 2006 as part of their first series of proprietary wiki platforms covering various entertainment industries. More than a wiki, CV was at launch a news and review website covering comics and offering discussion forums for users. This article examines how a semantic platform has developed to offer descriptive features catering to one industry (comics), using a proprietary architecture. Using the walkthrough methodological approach, I find that such practices, while not adhering to open-web standards, contribute to architectural design diversity (ADD). Standards in semantic data often push toward common grounds and exchange parameters. The ADD concept presented in this article focuses on highlighting divergent technical schemes in the computing sciences that do not rely on a few standards. I draw mainly on approaches and theories from information studies, grounded in contextual insights from communication studies and human-computer interaction.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".