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Record W4403639064 · doi:10.15353/joci.v20i2.5881

Towards Desirable Futures

2024· article· en· W4403639064 on OpenAlexvenueno aff
Marcus Foth

Bibliographic record

VenueThe Journal of Community Informatics · 2024
Typearticle
Languageen
FieldComputer Science
TopicInnovative Human-Technology Interaction
Canadian institutionsnot available
Fundersnot available
KeywordsFutures contractEconomicsFinancial economics

Abstract

fetched live from OpenAlex

The 20th anniversary of The Journal of Community Informatics signifies a milestone in the evolution of community informatics (CI) as a field dedicated to empowering communities through the strategic use of information and communication technology (ICT). This article offers some personal reflections on the origins and evolution of CI, tracing its roots to seminal works by scholars such as Michael Gurstein. It also tells the story of how urban informatics was inspired by CI as a distinct field of scholarship to study the interplay between people, place, and technology in urban environments. Building on this foundation, the present challenges and opportunities facing CI are explored, including issues of digital inclusion, ethical implications of emerging technologies, and the transformative potential of ICTs for social change. Looking ahead, the article envisions desirable futures for CI grounded in a life-centred approach that acknowledges the interconnectedness of humans and non-humans within larger ecological systems. Embracing a more-than-human paradigm, CI is uniquely positioned to advocate for ecological justice, amplify the voices of marginalised human and non-human communities, and foster collaboration between humans and the environment to create and protect resilient and sustainable habitat for life on this planet. Through these efforts, CI can contribute to a more just, equitable, and sustainable future for all living beings, averting the planetary ecocide that threatens our shared existence.

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)0.003
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.574
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0020.000
Research integrity0.0000.002
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 head0.045
GPT teacher head0.315
Teacher spread0.269 · 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 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
Published2024
Admission routes1
Has abstractyes

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