Bibliographic record
Abstract
With The Social Construction of What?, Ian Hacking aims to cool down the overheated debates around social constructionism (the 'science wars') by clarifying just what the phrase 'social construction' can be properly understood to mean. The book is a collection of previously published essays and lectures on a variety of topics, united not by a common argumentative thread but by this anti-polemical project. The first three chapters explore a series of approaches to specifying what is meant by the phrase 'social construction', and unpack the philosophical issues, or 'sticking points', raised by the application of social constructionism to the natural sciences. Chapters 4 and 5 develop the idea of 'interactive kinds', categories that interact with and alter the objects they label. Reframing social construction in terms of interactive kinds and looping effects helps to specify how a phenomenon can be socially constructed and real at the same time. Chapter 6 elaborates a distinction between 'forms of knowledge' and 'content of knowledge'. Hacking uses this distinction to assign relative roles to contingency and determinacy in the development of scientific knowledge. Chapter 7 applies the 'sticking points' developed in Chapter 3 to a case study of science-in-the-making, and Chapter 8 re-tells the story of the 'Captain Cook' controversy in a way that aims to defuse some of the tension. In this Review I will focus on Chapters 1-4, because these chapters introduce the concepts with which Hacking attempts to specify the meaning of 'social construction'; the later chapters are mostly applications of these concepts.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Science and technology studies Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
| gpt | Science and technology studies Domain: not available · Genre: Commentary About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | high |
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.009 | 0.104 |
| Scholarly communication | 0.022 | 0.022 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".