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Record W7113900509 · doi:10.55283/jhk.21237

Projects in the History of Knowledge

2025· article· en· W7113900509 on OpenAlexaff

Bibliographic record

VenueJournal for the History of Knowledge · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicWater Governance and Infrastructure
Canadian institutionsConcordia University
Fundersnot available
KeywordsVisionIgnoranceDynamismIntervention (counseling)Futures studiesSurprise

Abstract

fetched live from OpenAlex

The Panama Canal, the silkworm, a sugar plantation, the South Sea Bubble, an industrial workschool: they may seem to have little in common, but these, and other disparate interventions from around the globe, share a common epistemology relating knowledge and power in a specific dynamic: the project. A category new to the early modern period that still structures the world around us, the project offers tools for the critical and historical analysis of the large-scale, entangled changes that made modernity. Projects were, to borrow a phrase from Foucault, “technologies of power” that brought together, in a short span of time, many of the social mores, spiritual ends, and epistemic values associated with the modern world. Yet, even as projects claimed to offer new mechanisms for eradicating waste, solving problems, and realizing untold profits by mobilizing peoples and materials, they also engineered large-scale displacement and devastation. The history of projects centers not only the dynamism and ambition but also the violence and ignorance built into economically rationalized visions of the future. For projects were not the laughable schemes many satires suggested; they were integral to global capitalism, epistemic and financial risk-taking, labor exploitation, and environmental degradation. This special issue shows what their impacts were, how projects were configured as forms of knowledge, and how they interrelated with a global landscape of risk and possibility. Addressing their legacies requires critically exploring projects as modes of intervention in the world.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.508
Threshold uncertainty score0.776

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.047
GPT teacher head0.316
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 designNot applicable
Domainnot available
GenreOther

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

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