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Record W1910631659 · doi:10.29173/cmplct10022

Investigating Relationships: Thoughts on the Pitfalls and Directions

2011· article· en· W1910631659 on OpenAlexvenueno aff
Jeffrey W. Bloom

Bibliographic record

VenueComplicity An International Journal of Complexity and Education · 2011
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsEpistemologyPsychologyPhilosophy

Abstract

fetched live from OpenAlex

The feature article for this issue, entitled "(Re)Imagining Teacher Preparation for Conjoint Democratic Inquiry in Complex Classroom Ecologies," begins to carve out one aspect of the importance of relationship in the context of schooling. For the most part, the institution of schooling has ignored relationship. Instead, blaming teachers and students has become the modus operandi. Zero tolerance, accountability, among the many other sound bites in the politics of education categorically ignore the significance of relationship and its critical role not only in student-teacher dynamics, but also in all aspects of learning and personal growth. In fact, within the current political context, relationship is missing from the equation. Scripted curricula and requirements that teachers sign allegiance to these curricula, intensive teaching to the tests, the push for strict and specific national standards, and the desire to have all students working on the same "thing" (and not relationships of any kind!) at the same time, are all tremendous obstacles to developing interpersonal relationships in the classroom and to learning relationships. Of course, the notion of relationships is much more extensive than just those connections that exist between people. Relationships should be the material of what we learn and teach (Bateson, 1979(Bateson, /2002;; Since we are interested in complex systems, we need to see relationships as the material of systems, as well as to see relationships as systems themselves.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.641
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.238
GPT teacher head0.428
Teacher spread0.190 · 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 designObservational
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

Citations5
Published2011
Admission routes1
Has abstractyes

Explore more

Same venueComplicity An International Journal of Complexity and EducationSame topicAttachment and Relationship DynamicsFrench-language works237,207