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Record W7080060091

Effective Ways to Influence Appreciative Set Points? Deliberative Mini-Publics As Socio-Technical Systems Change Initiatives

2024· article· en· W7080060091 on OpenAlexaboutno aff

Bibliographic record

VenuePublication Database IASS (Institute for Advanced Sustainability Studies (IASS)) · 2024
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsnot available
Fundersnot available
KeywordsLotterySet (abstract data type)Context (archaeology)Value (mathematics)NarrativeAppreciative inquiryNatural (archaeology)Deliberation
DOInot available

Abstract

fetched live from OpenAlex

How might systems perspectives, including socio-technical systems change, cybernetics, and organization development, deepen our learning from existing experiments with sortition-based deliberative mini-publics? In this paper, I apply these lenses to the results of recent natural experiments where a cross-section of the larger system (a given socio-political system, not an organization) is invited to develop shared recommendations for a given policy area; in the process, participants generally also develop a greater sense of agency. Since these mini-publics can produce significant shifts in the system’s “imaginal field” or Overton window of possibilities, they can be seen as responses to Vicker’s call for ethical ways to influence the appreciative set-points of our socio-cultural system. Sometimes this influence occurs through large-scale narrative diffusion, as with the 1991 “People’s Verdict” sponsored by Maclean’s in response to a growing risk of separatism in Canada. Other times, built-in design elements increase the likelihood of sponsoring bodies adopting some or all of a microcosm’s recommendations, such as the feedback loops in the Citizens’ Council model from Vorarlberg, Austria. Yet even with a growing number of different formats worldwide and variations with regard to implementation, we see repeatedly that regular people, chosen by public lottery and offered a supportive interaction context, find value in exploring public issues together and work through differences to arrive at meaningful shared recommendations. Analogous to McGregor’s “theory X / theory Y”, a different pattern of behavior emerges in the context of intentionally designed and facilitated processes where each participant is respected and heard. Regardless of local design variants, in supportive contexts we repeatedly witness humans’ desire to take responsibility for improving collective life, and ability to navigate nuance and complexity.

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)0.064
metaresearch head score (Gemma)0.110
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.064
Threshold uncertainty score0.340

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0640.110
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0090.056
Scholarly communication0.0190.030
Open science0.0040.024
Research integrity0.0050.008
Insufficient payload (model declined to judge)0.0120.002

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 head0.046
GPT teacher head0.347
Teacher spread0.302 · 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 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
Published2024
Admission routes1
Has abstractyes

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