Against the Right to Work, For the Right to Contribute
Bibliographic record
Abstract
Is there a universal right to access some form of monetary income? And if so, what exactly does this right entitle one to? In the debate between establishing a right to work versus a universal basic income (UBI), defenders of the former sometimes appeal to the additional nonpecuniary benefits of work. In this article, I argue that the nonpecuniary benefits of work cannot serve as grounds to justify establishing a right to work rather than a UBI. To show this, I propose that rights must pass tests of efficacy and efficiency if we are to be justified in recognizing them. I then set aside the efficacy and efficiency of the right to work relative to our pecuniary interests and ask instead whether it displays these features in relation to a particular set of nonpecuniary interests. Ultimately, I show that the right to work is both ineffective at promoting these interests and inefficient when compared against a right to contribute without remuneration. As such, the nonpecuniary benefits of work give us little reason to prefer the establishment of a right to work when compared against some combination of a UBI and a right to contribute. The remainder of the article makes a positive case for recognizing a right to contribute by demonstrating the efficacy and efficiency of such a right in relation to certain nonpecuniary interests.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.033 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.014 | 0.005 |
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".