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Record W2297491993 · doi:10.14288/1.0093635

Kant's subject-object distinction

2011· article· en· W2297491993 on OpenAlexaff
Stephen Porsche

Bibliographic record

VenuecIRcle (University of British Columbia) · 2011
Typearticle
Languageen
FieldArts and Humanities
TopicPhilosophical Ethics and Theory
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSubject (documents)Object (grammar)Computer scienceEpistemologyPhilosophyArtificial intelligenceWorld Wide Web

Abstract

fetched live from OpenAlex

In chapters two and three of this thesis, the distinction between the subject and object of knowledge and perception in Kant's Critique of Pure Reason is examined in terms of what Kant calls, "representations." These representations are not, in general, as the name might suggest, pictures in the mind, or copies of objects. They are isolated bits of information which the mind has about the world; or, in other words, elementary ways in which the subject is related to the objects which it knows or perceives. The subject is constituted by the grouping of representations into different kinds of representations, mainly on the basis of similarities, so that we have the same sorts of information about different objects. The object is that which representations relate to when select representations of many different kinds are combined, mainly on the basis of coherence, so that we have different sorts of information about the same object. Chapter one is devoted to Kant's doctrine of the object in itself, which is discussed in terms of the distinction between knowledge and belief. Objects in themselves are objects apart from our representations of them. In spite of the fact that they cannot be known, objects in themselves are significant insofar as the false belief that we can know them is an inevitable result of the capacity of the subject to combine representations in different ways, including the combination of representations in the concept of an unknowable object.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.809
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.031
GPT teacher head0.172
Teacher spread0.141 · 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; a candidate call from one teacher head, not a consensus.

Study designOther design
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

Citations0
Published2011
Admission routes1
Has abstractyes

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