Bibliographic record
Abstract
Since the early 2000s, there has been an explosion in the usage of the term open, arguably stemming from the advent of networked technologies — including the Internet and mobile technologies. ‘Openness’ seems to be everywhere, and takes many forms: from open knowledge, open education, open data and open science, to open Internet, open medical records systems and open innovation. These applications of openness are having a profound, and sometimes transformative, effect on social, political and economic life.This explosion of the use of the term has led to multiple interpretations, ambiguities, and even misunderstandings, not to mention countless debates and disagreements over precise definitions. The paper “Fifty shades of open” by Pomerantz and Peek (2016) highlighted the increasing ambiguity and even confusion surrounding this term. This article builds on Pomerantz and Peek’s attempt to disambiguate the term by offering an alternative understanding to openness — that of social praxis. More specifically, our framing can be broken down into three social processes: open production, open distribution, and open consumption. Each process shares two traits that make them open: you don’t have to pay (free price), and anyone can participate (non-discrimination) in these processes.We argue that conceptualizing openness as social praxis offers several benefits. First, it provides a way out of a variety of problems that result from ambiguities and misunderstandings that emerge from the current multitude of uses of openness. Second, it provides a contextually sensitive understanding of openness that allows space for the many different ways openness is experienced — often very different from the way that more formal definitions conceptualize it. Third, it points us towards an approach to developing practice-specific theory that we believe helps us build generalizable knowledge on what works (or not), for whom, and in what contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.103 |
| Scholarly communication | 0.012 | 0.016 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".