Bibliographic record
Abstract
This article outlines a “sobjectivist” methodology that is specifically geared toward the constructivist style of reasoning. The main argument is that constructivist inquiries need to develop not only objectified (or experience-distant) but also subjective (experience-near) knowledge about social and international life. This requirement derives from the fact that constructivism is a postfoundationalist style of reasoning which emphasizes the mutually constitutive dialectics between the social construction of knowledge and the construction of social reality. By implication, a constructivist methodology should be inductive, interpretive, and historical. The methodical practice of sobjectivism follows a three-step logic from the recovery of subjective meanings to their objectification thanks to contextualization and historicization. A brief discussion of the security-community research program illustrates what sobjectivism looks like in practice. Overall, not only does the development of a consistent methodology systematize the practice of constructivist research, it also fosters engagement and dialog with other international relations approaches. By clarifying where constructivism falls on issues of validity, falsifiability, and generalizability, this article intends to enhance mutual legibility among competing methodologies.
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 | 0.103 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.005 | 0.068 |
| Scholarly communication | 0.019 | 0.016 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.002 | 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".