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Record W4411917512 · doi:10.25071/1916-4467.40844

Curricular Clouds of (Un)Knowing: Learning About Mystery From the Mystic Traditions

2025· article· en· W4411917512 on OpenAlexaffvenue
Holly Tsun Haggarty

Bibliographic record

VenueJournal of the Canadian Association for Curriculum Studies · 2025
Typearticle
Languageen
FieldArts and Humanities
TopicReligious Studies and Spiritual Practices
Canadian institutionsLakehead University
Fundersnot available
KeywordsMysticismSociologyEpistemologyPhilosophyArtLiterature

Abstract

fetched live from OpenAlex

Curricula have traditionally been concerned with proposing intentions and expectations for the teaching and learning of knowledge. What if curriculum conversed not only with the known, but the unknowable—with mystery? In this work, I investigate mystic traditions to learn about mystery. I come to a characterization of mystery as the dialectical condition of knowing while at the same time not knowing. Mystery is also an epithet of ultimate reality—a wholeness, which, all the while not completely knowable, nonetheless, attracts the utmost attention in its possibility for wisdom and meaningful transformation. The mystic traditions remind that mystery is an ineluctable feature of any metaphysical theory that aims to explicate ultimate reality, and because of this, belief is an inherent aspect of any metaphysical understanding. Furthermore, because metaphysical belief underpins our understanding of knowledge, a curriculum that invites mystery invites exploring, discerning and acting on existential beliefs. The mystic traditions proffer a way to approach mystery, namely the contemplative pathway, which can be engaged as both epistemological model and method. Mystic traditions also demonstrate how to deal with paradox, one of which is engaging reason to prepare for revelation. Thus, I conclude my essay with a poetic inquiry.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.267
Teacher spread0.239 · 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 designNot applicable
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
Published2025
Admission routes2
Has abstractyes

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