fenc.es — be wrong on the internet, productively

Trace the map of reasonable disagreement. Break arguments into statements, rate confidence and importance, and find the crux.

Objection on Twitter / X

Objection filed.— Objection (@objectionupdate) April 15, 2026

Most arguments scatter across papers, posts, and threads — and evaporate. A claim tree gives them a stable structure to gather against, the way a cathedral gathers centuries of work into a single, standing thing.

Territoires en redirection - imaginarium-s

Une enquête sur le renoncement comme compétence collective. Projet de recherche Imaginarium-s soutenu et financé par l'Institut pour la Recherche de la Caisse des Dépôts, la MAIF et la Ville de Grenoble 2025-2026. Réalisé par Alexandre Monnin, Claire Deligant, Emile Hooge, Romain Barrallon et Oscar Hooge. Une piscine qui ferme sans être reconstruite. Un projet routier abandonné après trente ans…

Objectionable

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Echopraxia (Firefall, #2)

Prepare for a different kind of singularity in this fol…

The Fiduciary Obligation

ROBERT FLANNIGAN; The Fiduciary Obligation, Oxford Journal of Legal Studies, Volume 9, Issue 3, 1 October 1989, Pages 285–322, https://doi.org/10.1093/ojls

Guerrero: Displaced by Way of the Fire of Drones

Invisible Forced Displacement in Mexico

eLife Claim Trees — eLife Claim Trees

Panel-level claim graphs for reproducibility — eLife Claim Trees

How to Fight Your Health Insurance Denial With an External Appeal — ProPublica

When a health insurer refuses to pay for your treatment, you may have the right to have the denial reviewed — and potentially overturned — by an independent provider. Here are six steps experts suggest to help you through the external appeal process.

Argumentation theory | Communication and Mass Media | Research Starters | EBSCO Research

<p>Argumentation theory explores the processes and methods of reasoning and debate used by individuals in both formal and informal contexts. The theory has roots in ancient philosophical discourse, particularly from figures like Aristotle, and has evolved through the contributions of modern philosophers such as Chaïm Perelman and Stephen Toulmin. It highlights how arguments are structured, identifying key components such as claims, grounds (or data), and warrants, which collectively help participants make their case. </p> <p>Additionally, arguments can be categorized into three main types: factual claims, which are verifiable; judgment or value claims, which are subjective; and policy claims, which pertain to proposed courses of action. This framework acknowledges the influence of personal biases, often shaping the reasoning process, and emphasizes the importance of logical support, backing, qualifiers, and rebuttals in strengthening arguments. In academic contexts, the theory suggests that creating valid topics should focus on policy arguments, while also addressing counterarguments to foster a comprehensive debate. Overall, argumentation theory serves as a critical tool for understanding how reasoning and persuasive communication function in various scenarios.</p>

Polly Wants a Better Argument

The “Stochastic Parrot” Argument is Both Wrong and Actively Harmful

Certifying and Removing Disparate Impact

What does it mean for an algorithm to be biased? In U.S. law, unintentional bias is encoded via disparate impact, which occurs when a selection process has widely different outcomes for different groups, even as it appears to be neutral. This legal determination hinges on a definition of a protected class (ethnicity, gender) and an explicit description of the process.When computers are involved, determining disparate impact (and hence bias) is harder. It might not be possible to disclose the process. In addition, even if the process is open, it might be hard to elucidate in a legal setting how the algorithm makes its decisions. Instead of requiring access to the process, we propose making inferences based on the data it uses.We present four contributions. First, we link disparate impact to a measure of classification accuracy that while known, has received relatively little attention. Second, we propose a test for disparate impact based on how well the protected class can be predicted from the other attributes. Third, we describe methods by which data might be made unbiased. Finally, we present empirical evidence supporting the effectiveness of our test for disparate impact and our approach for both masking bias and preserving relevant information in the data. Interestingly, our approach resembles some actual selection practices that have recently received legal scrutiny.