MétaCan
Menu
Back to cohort
Record W4414685850 · doi:10.47408/jldhe.vi37.1730

Exploring the relationship between theology and learning through the lens of disruption

2025· article· en· W4414685850 on OpenAlexfundno aff
J. L. Smith

Bibliographic record

VenueJournal of Learning Development in Higher Education · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicReligious Education and Schools
Canadian institutionsnot available
FundersAston UniversityKwantlen Polytechnic UniversitySouthampton Solent University
KeywordsConversationCurriculumSubject (documents)ComprehensionWork (physics)Active learning (machine learning)Experiential learning

Abstract

fetched live from OpenAlex

As in many other contexts, for theological education practitioners, successful teaching and learning outcomes not only include students’ clear comprehension of curriculum content but also the cultivation of skills to contextualise learning in multiple, unforeseen circumstances. In other words, academic achievement and personal/spiritual formation are inseparable. My research suggests that disruptive pedagogies (whether related to what is taught or how) are a foundational pedagogical tool that not only equip students to gain and understand new information but skills learners in practising the imaginative posture required to use their learning in real world situations. Against a larger backdrop, this conversation will explore: (a) The pedagogical and sociological factors implicated in (what I call) disruptive-inclusive learning, (b) How my work concerning the nature of the relationship between theology and pedagogy could contribute to a wider framework for considering the learning methodologies and methods indicated by a range of subject disciplines, and (c.) how such discussions, in turn, may lead to richer, more holistic and integrated approaches to LD more generally. - What are the biggest challenges in developing a dialogue between the how and the what of teaching and learning (i.e. curriculum content and T&L methodologies/ methods)? - What categories of learning disruption are specifically associated with different subject areas? As learning practitioners, do we equip learners to embrace or avoid these disruptions? - Did we/ what did we learn from the pandemic about operating in and preparing for the unknown and unforeseeable? What might the next phase of this be?

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)0.008
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.021
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.011
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.003
Science and technology studies0.0080.104
Scholarly communication0.0210.022
Open science0.0030.017
Research integrity0.0040.010
Insufficient payload (model declined to judge)0.0040.000

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 head0.182
GPT teacher head0.389
Teacher spread0.207 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Learning Development in Higher EducationSame topicReligious Education and SchoolsFrench-language works237,207