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Record W2917265591 · doi:10.33137/js.v2i0.31032

Redrafting the Ontology of Scientific Change

2018· article· en· W2917265591 on OpenAlexaffvenue
Hakob Barseghyan

Bibliographic record

VenueScientonomy Journal for the Science of Science · 2018
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOntologyNormativeEpistemologySet (abstract data type)Perspective (graphical)Computer scienceEncyclopediaScientific theoryClass (philosophy)Artificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Recent developments in theoretical scientonomy coupled with a reflection on the practice of the Encyclopedia of Scientonomy all suggest that the ontology of scientific change currently accepted in scientonomy has serious flaws. The new ontology, suggested in this paper, solves some of the issues permeating the current ontology. Building on Rawleigh’s suggestion, it considers a theory as an attempt to answer a certain question. It also introduces the category of definition as a subtype of theory. It also reveals that methods and methodologies of the currently accepted ontology do not differ from the perspective of their propositional content and, thus, belong to the same class of epistemic elements. This is captured in the new definition of method as a set of criteria for theory evaluation. It is also argued that methods are a subtype of normative theories. It is shown that normative theories of all types, including methods, ethical norms, and aesthetic norms, can be both accepted and employed. Finally, a new definition of scientific mosaic is suggested to fit the new ontology. Suggested Modifications [Sciento-2018-0005]: Accept the following definitions of method and methodology: Method ≡ a set of criteria for theory evaluation. Methodology ≡ a normative discipline that formulates the rules which ought to be employed in theory assessment. Reject the previous definitions of method and methodology. [Sciento-2018-0006]: Accept the following ontology of epistemic elements, where: Each theory is an attempt to answer a certain question. Theories can be of three types – descriptive, normative, or definitions. Method is a subtype of normative theory. Questions as well as theories of all types – including methods – can be accepted. Normative theories of all types can be employed; the name of the stance is norm employment. Accept the following definition of theory acceptance: Theory acceptance ≡ a theory is said to be accepted by the epistemic agent if it is taken as the best available answer to its respective question. Also accept the following questions as legitimate topics of inquiry: Role of Definitions in Scientific Change: Do definitions play any distinct role in the process of scientific change, or do they only exhibit the exact same patterns as descriptive and normative theories? Reducibility of Definitions: Are definitions a distinct subtype of theory, or are they somehow reducible to descriptive theories and/or normative theories? Reject the previous ontology of epistemic elements and the previous definition of theory acceptance. [Sciento-2018-0007]: Accept the following definition of definition: Definition ≡ A statement of the meaning of a term. [Sciento-2018-0008]: Provided that modification [Sciento-2018-0006] is accepted, accept the following definition of norm employment: Norm Employment ≡ a norm is said to be employed if its requirements constitute the actual expectations of the epistemic agent. [Sciento-2018-0009]: Accept the new definition of scientific mosaic: Scientific Mosaic ≡ a set of all epistemic elements accepted and/or employed by the epistemic agent. Reject the previous definition of scientific mosaic. [Sciento-2018-0010]: Accept that: Epistemic stances of all types can be taken explicitly and/or implicitly. Epistemic elements of all types can be explicit and/or implicit. Accept the following question as a legitimate topic of inquiry: Tracing Implicit/Explicit: Should observational scientonomy trace when a certain stance towards an epistemic element was taken explicitly or implicitly? What are the practical considerations for and against collecting and storing this data?

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 imitation

Not 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.

metaresearch head score (Codex)0.025
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.993
Threshold uncertainty score0.134

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.013
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0070.004
Science and technology studies0.0070.086
Scholarly communication0.0150.029
Open science0.0030.010
Research integrity0.0060.013
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.259
GPT teacher head0.369
Teacher spread0.110 · 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 source (direct Gemma or distilled Codex), not a consensus.

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

Citations13
Published2018
Admission routes2
Has abstractyes

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Same venueScientonomy Journal for the Science of ScienceSame topicPsychology of Moral and Emotional JudgmentFrench-language works237,207