Bibliographic record
Abstract
Icebergs, at present, are living a second life on screens. While they are one of the natural world’s most photogenic objects, icebergs are also subject to modes of representation through parametric modeling applications. The purpose of this digital life on screens is largely confined to determining how, and under what conditions, icebergs can be made a source of potable water for the planet. Yet icebergs have a story to tell about the epistemological and economic production of northern natural resources. Distinct institutional actors, from oceanographers and military engineers to Saudi royalty and software design companies, have sought to control and come to know icebergs through specific practices of modeling. I argue that the representation of icebergs is a contingent practice that has often been bound up with processes of commodification. To come to know icebergs we have to come to know how these quintessentially polar phenomena have been represented and commodified, across the twentieth century and at a significant remove from the highest latitudes of the planet. The increasing pace of northern development, with natural resources at the vanguard of corporate and governmental incursions, signals the emergence of “media environments” that are extending the representation of (and control over) natural phenomena through a series of media technologies, from 3D modeling applications and collections of satellite data to virtual reality environments and predictive algorithms.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.145 | 0.049 |
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".