Bibliographic record
Abstract
In the exercise of any human skill, any craft, there are particular matters that require special attention, that demand we take care . When driving, for example, we know that merging on to a busy freeway, or passing on a two-ane highway, demands heightened focus. Likewise, in interpersonal relations, there are situations that call for extra care, for special tact. One way to characterize intellectual crafts, then, is to identify their forms of care: to which matters does the craftsperson offer focused attention? This chapter will examine some of the forms of care that characterize a non-binary policy analysis. I will first argue that a traditional concern with avoiding the deduction of values from facts and vice versa is replaced in a non-binary approach by careful exploration of the variety of fact and value claims that underlie any policy position. A non-binary approach should also lead us to pay close attention to the patterns of attention behind any piece of policy analysis, and critically examine the types of trust that support the analysis, and whether that trust is warranted. A non-binary approach must be attentive to the ways in which language shapes our reality: to the uniting or sundering of phenomena by our conceptual categories, for example. Section six further argues that a non-binary approach protects even certain normative goods associated with the binary view, better than does the binary view itself. From is to ought, and back again In their 2012 polemic, Economists and the Powerful , Norbert Haring and Niall Douglas discuss John R. Commons's 1893 book, The Distribution of Wealth : He argued that wealth distribution is the result of state policy, notably state regulation and legal rules, which protect and define property rights. He detailed the role that legal rules play in shaping the distribution of negotiation power and the distribution of income. Commons’ argument was that state-created entitlements like monopolies, patent, copyright and franchises enable their owners to restrict supply and raise the price of the goods they sell. Since the state is one of the most important determinants of the relative values of goods, Commons argued, the state is implicated in income distribution and should intervene to improve the bargaining power of the weaker groups.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.008 | 0.030 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".