MétaCan
Menu
Back to cohort
Record W2985022911

Making Sense Of Life In 2017 - Metaphors Of The Internet

2017· article· en· W2985022911 on OpenAlexaff
Annette Markham, Jessa Lingel, Kristian Möller, Kevin Driscoll, Katie Warfield

Bibliographic record

VenueAoIR Selected Papers of Internet Research · 2017
Typearticle
Languageen
FieldComputer Science
TopicDigital Communication and Language
Canadian institutionsKwantlen Polytechnic University
Fundersnot available
KeywordsCyberspaceMetaphorThe InternetConstruct (python library)RhetoricSociologyValue (mathematics)IdeologyPoliticsEpistemologyInternet privacyWorld Wide WebComputer sciencePolitical science
DOInot available

Abstract

fetched live from OpenAlex

Various metaphors are used to make sense of and explain our experiences with and in digital, web, internet-mediated, or technologically saturated contexts. These thrive and dwindle as our rhetoric moves from Cyberspace and the Electronic Frontier to the World Wide Web, social network sites and networked publics. The more ubiquitous networked communication becomes, the more it is articulated as a way of being, and the more banal and invisible its properties become. Studying metaphors offers important insights about how people and groups make sense of their experience, construct the world and attribute value to various phenomena. Yet, whatever metaphor is chosen, it concurrently illuminates and distorts what we see of the world and how we understand it. This panel brings together five presentations that engage with the three-pronged metaphorical framework of the internet as a tool, a place and a way of being suggested by Annette Markham in 1998. Today, 20 years later, authors examine various commonly invoked metaphors about the internet, networked technology or socially mediated experiences and utilize or extend the original framework. Combined, the papers explore the metaphors used by people and in large discourse; how dominant metaphors shift in time, and the political, ideological and methodological implications of these metaphors.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.401
Threshold uncertainty score0.799

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0040.002
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 head0.102
GPT teacher head0.382
Teacher spread0.280 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueAoIR Selected Papers of Internet ResearchSame topicDigital Communication and LanguageFrench-language works237,207