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Record W3047779673 · doi:10.3138/tjt-2020-0105

Taking on Privilege and Oppression in Practical Theology: A Spatial Perspective

2020· article· en· W3047779673 on OpenAlexvenueno aff
Jaco S. Dreyer

Bibliographic record

VenueToronto Journal of Theology · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicReligion, Society, and Development
Canadian institutionsnot available
Fundersnot available
KeywordsOppressionPrivilege (computing)SociologyPerspective (graphical)ScholarshipEconomic JusticePower (physics)EpistemologySpace (punctuation)Context (archaeology)LawPhilosophyPoliticsComputer sciencePolitical science

Abstract

fetched live from OpenAlex

Practical theology has a legacy of privilege and oppression. This is a point of departure for Courtney Goto in Taking on Practical Theology. Goto convincingly argues that what counts as practical theological knowledge is shaped by a dominant paradigm that privileges some and marginalizes others. This is particularly obvious in the way practical theologians approach “context,” a key feature of today’s practical theological scholarship. To deal with privilege and oppression in practical theology, it is necessary to use C/critical, I/intersubjective spaces in order to create a compassionate, just, and responsible community of practical theologians. My thesis is that Goto’s analyses and conclusions could be enriched and strengthened by a spatial perspective. I introduce Henri Lefebvre’s notions of the production of space and the trialectics of space to illustrate the possible contribution of such a spatial perspective. A view of social space in terms of perceived, conceived, and lived space helps us to see a more differentiated picture of the power dynamics in social (and thus also academic) practices. Last, I introduce Edward Soja’s ideas on spatial justice. His views add even more analytical power to the spatial perspective and support our normative vision of justice as integral element of our scholarly practices.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score0.993

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 studies0.0000.000
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 head0.036
GPT teacher head0.361
Teacher spread0.325 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2020
Admission routes1
Has abstractyes

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