Bibliographic record
Abstract
In this chapter, I discuss and defend objectivity as a critically valuable norm for how people ought to communicate information publicly, especially information about or bearing upon human activities. This norm plays a central role in the natural and social sciences, in public media, and in government and courts of law. In this essay, I will focus especially on the role of this norm in the social sciences, while recognizing its critical value for all public communication. It is especially important at present to understand and defend the norm of objectivity because of diverse critiques and misunderstandings. This chapter reviews and responds to some of these. The chapter observes that the norm of objectivity is valued because it functions to produce reliable information, which in turn facilitates problem-solving. The argument in defense of objectivity proceeds through two steps. First, the chapter examines and criticizes some widely held assumptions regarding the standard understanding of the norm of objectivity. Then, the chapter moves on to define and defend an alternative way of understanding the norm of objectivity, which it refers to as the civic model of objectivity. Accordingly, this model of objectivity especially calls for relevant information to be communicated as an ongoing intelligible, reasonable, and inherently reciprocating public activity. In keeping with these expectations, the chapter further recognizes that value judgments affect how researchers identify, interpret, and communicate reliable and accurate information. The chapter describes how to make and communicate these judgments in keeping with the norm of objectivity, rightly understood. Although the approach to objectivity defended in this chapter may seem new and unorthodox, a strong case can be made for arguing quite the opposite. The chapter ends by arguing that in broad outline the position defended here was first outlined in Weber's classic essay on objectivity, which he wrote in 1905.
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 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 | 0.058 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.009 | 0.118 |
| Scholarly communication | 0.020 | 0.030 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.011 | 0.025 |
| Insufficient payload (model declined to judge) | 0.003 | 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".