Basic Contents and Contributions of Hacking’s Experimental Realism
Bibliographic record
Abstract
Based on the debate between scientific realism and anti-scientific realism and philosophy of science is too theoretical, Hacking put forward experimental realism. Experimental realism illustrates the importance of experiments in science, and put forward its four basic propositions: (a) Experiment is the basis of science. (b) The experiment entity operable. (c) The content of experiment is operating experimental entity. (d) Experiment entity causal attributes the fundamental basis of experimental research. “The experiment has its own life” is a special proposition and its connotation is: (a) Experimental entity is independent of the specific theoretical certainty and stability. (b) The experiment system has its own independent status and structure. (c) Laboratory activity and result are decided by its own true nature. Experimental realism resolved the conflict between scientific realism and anti-scientific realism, broke the theorical tradition, understood the relation between the experiment and theory, and promoted the development of scientific practice philosophy.
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 | 0.020 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.046 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.007 | 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".