The Performativity of Language in Real and Imagined Spaces: Locative Media and the Production of Meaning
Bibliographic record
Abstract
Harold Innis ’ dialectic of time and space-based media—where time-based media is fixed and material and space-based media is dynamic and mobile—finds a particular synthesis in various forms of spatial annotation whereby messages, notes, stories and histories can be digitally associated with various places. In this paper I examine how two locative projects, Toronto’s [murmur] and London’s Urban Tapestries, accrete stories over time that performatively define places, their use, and their affective associations. This process of creating a spatial ontology is both iterative and emergent; users add and edit content at different stages to create multiple linguistic, descriptive maps of a place which contribute to its overall social meaning. The annotation projects I examine are simultaneously time and space based media, depending as they do on material sites and digital, narrative descriptions. As a hybrid media, they have a great deal to tell us how we describe meeting to places and objects over time, as well as providing parallel insights into the structural processes of meaning-production itself. For Herald Innis, time-biased media are those media which are durable and heavy, resisting the ravages of time. They endure over the centuries and symbolize a triumph over temporal existence, as was the case with the pyramids and stone tablets of the ancient Egypt
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.094 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| 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".