The Allegory of the Rock Engineering Cave
Bibliographic record
Abstract
Abstract Geoscientists and engineers are part of a system that produces changes to the world we live in. As machines are expected to gradually replace humans in various technical tasks (e.g., data collection and data characterisation), it becomes crucial to apply critical thinking and to question the foundations of commonly accepted practices. The challenge is to accept that empirical methods are shaped by cognitive biases, which result from our mind interpreting data by a process of data simplification. Indeed, it is possible to draw an analogy between rock engineering methods and Plato’s Allegory of the Cave. The fire casting the shadows along the cave walls represents the process of quantification of qualitative assessments of commonly accepted data collection methods. The chains holding the engineers as prisoners in the cave are empirical methods accepted as industry standards despite important limitations. Engineering judgment alone will not allow engineers to break free of those chains, and to emerge from the confined spaces of the cave and see things for what they really are (i.e., introduce truly objective data collections methods that better reflect failure mechanisms). In this paper the authors use philosophical arguments to justify the need to “dequantify” the GSI classification system and to teach us that engineering design should always be driven by the important questions of how, why, and whether.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.015 | 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".