MétaCan
Menu
Back to cohort
Record W2004269233 · doi:10.5210/fm.v18i5.4529

Navigating an imagined Middle–earth: Finding and analyzing text–based and film–based mental images of Middle–earth through TheOneRing.net online fan community

2013· article· en· W2004269233 on OpenAlex

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.

Bibliographic record

VenueFirst Monday · 2013
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Text Analysis Techniques
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPoint (geometry)Adaptation (eye)Mental imageDisseminationSocial mediaWorld Wide WebComputer scienceSociologyPsychologyCognitionMedia studiesMathematics

Abstract

fetched live from OpenAlex

The proliferation of social media brings new opportunities to discover the ways in which we receive, process, and disseminate information — even information that seems confined to our imaginations. Mental imagery — those images we create in our imaginations as we read a text or watch a film — is not well understood. Netlytic, a Web-based system for automated text analysis, permitted the capture and analysis of online discussions relating to mental images of J.R.R. Tolkien’s and Peter Jackson’s The Lord of the Rings as text and as film adaptation, giving insight to our understanding of mental imagery as a form of human cognition and information processing. Furthermore, this study serves as a starting point for further development of academic research using Web-based text analysis systems and online communities.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.388
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.003
Open science0.0030.002
Research integrity0.0000.002
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 head0.055
GPT teacher head0.318
Teacher spread0.263 · 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