Information-not-thing: further problems with and alternatives to the belief that information is physical
Bibliographic record
Abstract
In this short paper, we show that a popular view in information science, information-as-thing,fails to account for a common example of information that seems physical. We then demonstrate how thedistinction between types and tokens, recently used to analyse Shannon information, can account for thissame example by viewing information as abstract, and discuss existing definitions of information that areconsistent with this approach. Dans ce court article nous montrons qu'une vision populaire en sciences de l'information,l'information en tant qu’une chose, échoue à rendre compte d'un exemple commun d'information quisemble physique. Nous démontrons ensuite comment la distinction type/token, utilisée récemment pouranalyser l'information de Shannon, peut rendre compte de ce même exemple en considérant l'informationcomme abstraite, et nous discutons des définitions existantes de l’information qui sont compatibles aveccette approche.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.005 | 0.032 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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; both teacher heads agree on what is shown here.
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".