Critical Accounting for the Hidden Costs of Knowledge Production
Notice bibliographique
Résumé
Keynote lecture at the Critical Perspective on Accounting Conference, Universida Nacional de Colombia 2023<br> https://fce.unal.edu.co/cpa2023/ Critical Accounting for the Invisible Costs of Knowledge Production A handful of Western-based for-profit conglomerates dominate the present-day global academic publishing industry. This state of affairs has led to a highly inequitable, exclusionary, exploitative, and opaque system, ultimately enclosing public knowledge by an immensely profitable publishing oligopoly. This is a familiar story we have been hearing for some time. And despite various calls for reform, most notably the Open Access movement, the position of the publishers remains deeply entrenched. In this talk, I tell a story of power, of how corporate publishers have transformed into self-appointed governance bodies over local, national, and global knowledge production. They achieve this by establishing norms, standards, and metrics that confer prestige and enforce compliance on researchers striving for advancement in an intensely commodified and competitive yet artificial "international" market, supported by the mantras of academic capitalism and the globalized knowledge economy. Taking advantage of the digital turn and network effects, corporate publishers have been building "full-stack" or end-to-end digital platforms, exerting their influence on all aspects of the knowledge life-cyle, or supply chain, from research to publication, data curation, knowledge circulation, and certification. By encoding their profit-driven norms and standards into the very infrastructure that researchers rely on, they wield significant power over academic labour in ways that often go unnoticed but come with real, though poorly recognized costs and harms. These include dependence, epistemic injustice, loss of rights to research, homogenization and reduction of bibliodiversity, and decontextualization of local knowledge systems and traditions. Through case studies of mergers and acquisitions, including Elsevier, Clarivate, Spring-Nature, and the knowledge cartel they formed, I illustrate how these publishers capitalize on the extraction, collection, and analysis of big data and researcher-generated data traces to create new markets and shape "values" in the form of predictive analytics that researchers and institutions seek to enhance their global rankings—another powerful tool of governance in private hands. Significantly, the design of these platforms carries colonial and structural biases, empowering the already "rich" in scholarly capital and institutional advantages while pushing the scholarly marginalized and diverse knowledge systems further toward the epistemic peripheries. Despite the potential of Open Access and Open Science to democratize and pluralize knowledge, uncritical market-based thinking has been easily co-opted by the corporate agenda, further exacerbating deep-seated structural inequities in knowledge production. In this regard, the calls for fair and transparent pricing for publishing services detract from the real crux of the problem: contestation over structural power and who has the authority to set the knowledge production agenda, which in turn dictates how government and institutional resources are distributed, and how academic labour are valued. Considering the many unsuccessful attempts to reclaim the knowledge commons and co-create open infrastructure, my talk concludes with a call for new imaginaries and narratives of open and pluriversal scholarship, including exploring new metrics and accounting paradigms to consider the neglected costs and values of academic knowledge production. This is crucial in acknowledging the hidden costs and promoting a more equitable, localized, and diverse knowledge ecosystem. I call for exploring fundamentally relational, globally interoperable, yet locally independent community-governed infrastructure as a potential solution to the current challenges. Alliance-making with diverse social movements beyond the university that share the common values and principles of care-full relationships and stewardship of our knowledge as a common resource is needed now.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,008 | 0,032 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; les deux têtes enseignantes s’accordent sur ce qui est montré ici.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».