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Record W2278369260

On the Possibility of Measuring Freedom: A Kantian Perspective

2011· preprint· en· W2278369260 on OpenAlexfundno aff
Sebastian Silva-Leander

Bibliographic record

VenueOxford University Research Archive (ORA) (University of Oxford) · 2011
Typepreprint
Languageen
FieldSocial Sciences
TopicIncome, Poverty, and Inequality
Canadian institutionsnot available
FundersEconomic and Social Research CouncilAustralian Agency for International DevelopmentInternational Development Research CentreYale UniversityUnited Nations Development ProgrammeRobertson FoundationUNICEF
KeywordsUnobservableNormativeEpistemologyAutonomyCapability approachPerspective (graphical)Counterfactual thinkingProcess (computing)SociologyComputer sciencePhilosophyPolitical scienceLawArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

<p>More than three decades after Sen’s first formulation of the so-called “capability approach”, practitioners have yet to measure a capability set. This raises fundamental questions about the empirical viability of Sen’s approach. In this paper, we argue that Kantian philosophy may offer valuable insights into how to deal with this problem, as the methodological difficulty which has hampered the full operationalisation of the capability approach lies at the heart of Kant’s philosophical system. In particular, we will argue that the Kantian notion of autonomy freedom may offer a viable alternative to Sen’s notion of opportunity freedom for the operationalisation of an internally coherent normative framework that is compatible with Sen’s representation of human nature. This allows us to propose an operationalisation strategy that focuses (1) on the normative content of choices, and (2) on the process of decision-making, rather than on the measurement of unobservable and counterfactual opportunities.</p>

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.552
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0030.007
Scholarly communication0.0000.000
Open science0.0050.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.091
GPT teacher head0.309
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2011
Admission routes1
Has abstractyes

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