Making Sense Of Life In 2017 - Metaphors Of The Internet
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".