Praticando metodologias para análises de conjuntura
Bibliographic record
Abstract
Conjunctural analysis is a rather enigmatic practice, observed mostly after the fact and in feats of exemplary execution, apparently somewhat resistant to codification, and maybe too modish for methodological rules. Predicated on the analysis of politically salient ‘situations’, conjunctural approaches combine reflexive theorizing with socially engaged inquiry and context-rich, historicized modes of analysis. Exploring the potential of conjunctural analysis in economic geography, the article moves first to tease out the methodological implications of this rather elusive approach, including: attention to complex states of causal codetermination, ‘in articulation’; an orientation to relational and ‘unbounded’ modes of inquiry; an emphasis on the stress-testing of received theory claims and conceptual categories, often in anomalous (as opposed to ‘typical’) situations; and a commitment to reflexivity, context-engaged analysis, and ‘thick’ theorization. Second, the article interrogates these methodological dispositions and propositions by placing them in dialogue, indicatively, with the case of Chinese capitalism. This is a case that routinely frustrates and confounds extant theoretical frameworks and conceptual categories, sometimes prompting theoretical defeatism. Although conjunctural analysis is not methodologically prescriptive, it implies distinctive criteria for problem formulation and research design; for ‘casing’, case selection, and specification; and for the exposition and (re)construction of contextualized theory claims.
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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 | 0.028 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.019 | 0.004 |
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".