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Record W7014084310

Open Pharma: Collective Action to Common Pharmaceutical Knowledge

2023· other· en· W7014084310 on OpenAlexfundno aff

Bibliographic record

VenueeScholarship (California Digital Library) · 2023
Typeother
Languageen
FieldSocial Sciences
TopicInformation Systems Theories and Implementation
Canadian institutionsnot available
FundersOffice of ScienceNational Institutes of HealthMcGill University
KeywordsCollective actionAction (physics)TRACE (psycholinguistics)Social movementPoliticsVariety (cybernetics)EthnographySociology of scientific knowledgeFrame (networking)
DOInot available

Abstract

fetched live from OpenAlex

Access to medicines is a critical ongoing challenge to advancing goals of health equity. Recent changes in the political economic and technoscientific domains of pharmaceuticals beg a reexamination of shifting processes in this space, especially emergent forms of collective action to address structural conditions for making new and old drugs. In particular, two trends in science—open science and community biology—have created the social and technical conditions through which new alternative imaginaries have emerged to research, develop, and make medicines. This dissertation offers an ethnographic account of these actions to common pharmaceutical knowledge through open science and lay participation in drug research. I examine two sites: the first site is a diffuse network of academic and nonprofit initiatives applying open science to drug research and development; the second site is a citizen science initiative, Open Insulin, leveraging a direct social action approach to make insulin in a community lab.Drawing on 29 in-depth interviews, over 300 hours of observations of citizen scientists’ organizational and research activities, and content analysis of journal articles and university and nonprofit organizations’ policies and websites, I trace a burgeoning movement to apply “open” principles and practices to the research and making of pharmaceuticals, an area I refer to as open pharma. I begin with an analysis of this open pharma movement by examining three key characteristics. First, I identify the major narratives discursively employed by actors to frame the movement and provide rationales to mobilize others, often drawing on market logics. Next, I trace the active building and institutionalizing of open pharma through the establishment of organizations and open sharing policies. Then I reveal sites of resistance actors experienced in university settings related to publishing and commercialization imperatives—which often translated to patent imperatives. My next set of findings focus on Open Insulin and the connections between their organizational structure and goals for creating more egalitarian alternatives to corporatized science practices and logics. I surface how membership and decision-making authority acted as key nodes of tension and change within the group, and I illustrate the project’s mission as continuously constructed in relation to these nodes. Finally, I further explore the discursive production of Open Insulin’s mission through two competing visions for making affordable insulin: an unprecedented but more transformative approach for “community manufacturing” through medicine cooperatives, and a more common and socially legitimized approach through a contract manufacturer partnership. As group members organized toward these visions, I unpack specific challenges groups face in looking to resist processes of capitalization in highly technical and regulated domains such as pharmaceuticals. \nThrough my tracing of movement practices and aims, I illuminate important entanglements between divergent approaches to social change, markets, regulatory regimes, and technoscientific infrastructure that construct and value openness in pharmaceuticals. This research articulates alternative imaginaries for how to organize biomedical knowledge production, and how they variously shape projects to intervene in inequities perpetuated by the political economy of health and illness.\n

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 categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.037
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0040.004
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.027

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 head0.082
GPT teacher head0.392
Teacher spread0.310 · 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; both teacher heads agree on what is shown here.

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
Published2023
Admission routes1
Has abstractyes

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