The Last Great Sea: A Voyage through Human and Natural History of the North Pacific Ocean, by T. Glavin [Review]
Bibliographic record
Abstract
will act as the foundation of research.What is known (axioms), what one wishes to know (postulates), and how one intends to collect data that will assess postulates (data statements) are critical first steps in research.The clarification of ecological concepts is important as well.Ecology often focuses on what Ford terms integrative concepts (e.g., stability, ecological integrity, resilience), theoretical constructs about ecological organization that defy direct measurement and demand synthesis from a variety of system studies.Caution is directed to the uncritical use of statistical inference as the sole assessment of postulates.Highly significant is the notion of theory domains which define the boundaries within which a particular theory may or may not operate, effectively rendering ecological theory development very complex.Of significance as well is the notion that both measurement and experiment are essentially an art.Even when precise and accurate, all measurements by definition are abstracts in that they represent the object of interest, but are not the things themselves.Ford refutes the notion that Popperian falsification can be used as the basis for theory change, citing evidence to show that the rational basis for theory acceptance or rejection 1s much more complex.The reader is introduced to the "subjective" or sociological dimensions of science.Science is presented as not simply an automated, invariant or determined process, but as a human activity under-MISCELLANEOUS The Last Great Sea: Vol.116
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".