Do not make snap decisions about what you are seeing: how digital analysis of the images from the Canadian Shield highlights the difficulties in classifying shapes
Bibliographic record
Abstract
The act of classification has the widest implications for scholarship. Whatever the format, it involves the totality of our being. The use of our eyes indicates that decisions about whatever it is that we observe have already been made. Yet the interaction between the mechanical act of seeing and the mind or memory has rarely been registered. An object once seen implies that the researcher's consciousness is engaged. The description of mere shape records that interaction. To establish whether sub-conscious decisions have been made as to the meaning of a shape, it might be placed in an armature. VIPS/ip software, created by both computer scientists and art historians, provides such an armature. The separate roles played by memory, brain, and eye in engaging with the shapes, encountered on the pictograph sites of the Lake of the Woods might then be detected. Subsequent labelling which bears these roles in mind just might isolate the contribution made by memory. The systematic identification and cataloguing of such images by an investigator may also enable us to understand something of the intricate and uncharted past of the Canadian Shield and about ourselves.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.013 | 0.019 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".