Brett Reynolds

Homeostatic property cluster theory gave natural-kinds realism a powerful anti-essentialist form, on which a kind can be real because its properties cluster through mechanisms rather than because it has an essence. In the HPC-descended debate, the homeostatic component can be made to carry too much weight when asked to cover several achievements that aren't the same: reliable co-occurrence, network order, maintenance, and feedback-sensitive control. This paper treats projectibility as the target of kind claims and offers a diagnostic ladder: identify the projection target, then ask whether a stable profile supports it, whether network order makes the profile non-accidental, what if anything maintains the profile, and whether that maintenance is corrective control. On the disciplined use proposed here, homeostasis belongs at the last step, where perturbation-sensitive organization preserves a higher-scale relation through lower-scale variation. The Watt governor calibrates control, glycemic regulation tests the strict biological version of the control diagnostic, interjection tokens supply a contrast linguistic case, and monitored agreement production supplies a candidate positive one. The result is a disciplined way of using the framework's insight without turning homeostatic into a loose synonym for stable or projectible.

Social status-assignments can support projection without moral warrant. From a promise we infer duty and claim; from release, lapse; from judgment, procedural consequences; from discriminatory standing, credibility loss, exclusion, or anticipatory adjustment. Extending Khalidi (2018)’s account of projectible kinds, I argue that such inferences travel through causal-normative networks: typed structures joining causal uptake, status transition, recognition, and constitutive ties within a practice or field. The payoff is an account of how status-assignments can be socially effective, institutionally valid or communally conferred, enforceable or final, recognitionally mistaken, and morally unwarranted in different combinations.

The word language names several distinct objects across the language sciences: capacity, idiolect, communicative practice, population convention, named sociohistorical object, and formal system. Pluralist accounts (especially Santana's distinction among psychological, social, and abstract ontologies) show why monism fails but don't say when a proposed sense licenses inference or when evidence about one sense can travel to another. This paper adds a homeostatic property cluster (HPC) discipline: a sense earns realist standing where its properties are stabilized by identifiable mechanisms and project for a field's purposes; where the mechanisms diverge, the shared label doesn't license transfer. The map distinguishes three categories proper (population convention, named sociohistorical object, abstract formal system) from three neighbourhoods of substantively coupled constituent categories (capacity, idiolect, communicative practice). The payoff is diagnostic. The Casten / Pinker exchange over genomic evidence for spoken-language ability shows how developmental verbal-performance evidence gets read as evidence about a uniquely human language faculty; similar slippages arise when census recognition is read as convention, formal models as cognitive architecture, or analyst diagnostics as the object. The umbrella label remains useful for coordination, but the load-bearing scientific units are the categories and neighbourhoods, not the umbrella.

This paper argues that grammaticality is best understood as a homeostatic property-cluster (HPC) kind anchored in morphosyntactic form–meaning pairings and regimented by community practice. The framework distinguishes objective grammatical status – whether the community licenses a form–meaning pairing – from the subjective feeling of ungrammaticality, which integrates grammaticality with processing costs, social evaluation, and prescriptive norms. Drawing on contemporary metaphysics of science, we show how grammaticality exhibits the key features of HPC kinds: a cluster of co-occurring properties (morphosyntactic well-formedness, semantic compatibility, community entrenchment), homeostatic mechanisms that maintain cluster stability (language acquisition, repair strategies, sociolinguistic policing), and systematic projectability supporting inductive generalization. Processing constraints shape what patterns can be licensed through acquisition and transmission while also affecting how licensed patterns feel during comprehension. This analysis clarifies why grammaticality supports explanatory and inductive generalization despite surface variation and gradience. The HPC framework predicts which phenomena will pattern with grammaticality (morphosyntactic violations, semantic mismatches at the syntax-semantics interface) versus those that won’t (pure phonotactic violations, processing overload). By grounding grammaticality in both cognitive mechanisms and social practices, the account reconciles the apparent universality of particular constraints with the evident community-relativity of others. The framework has implications for methodology (validating judgment tasks while acknowledging their limits), theory (reconciling generative and usage-based evidence), and typology (predicting which “exotic” grammars can exist as stable linguistic systems).

Coelho Mollo and Millière pose the Vector Grounding Problem: how the internal states of large language models could acquire extra-linguistic reference. They answer it with a two-condition teleosemantic account requiring causal-informational coupling to the world and a selection history that gives states the function of carrying that information. This reply accepts those teleosemantic foundations but argues that what the grounding debate calls “referential grounding” is itself a cluster concept. It bundles at least four distinct relations: teleosemantic selection, causal-informational coupling, communicative calibration, and inferential-role structure. Drawing on homeostatic property cluster theory, I replace the binary grounded/ungrounded verdict with a groundedness profile that records where a system sits on each strand. The payoff is explanatory and predictive: the profile decomposes the broader bundle often circulating under the name “grounding” into stabilizers that predict robustness, corrigibility, and characteristic failure under correction, prompt pressure, and domain shift in ways a binary verdict can't.

The debate over whether large language models “really think” reproduces a familiar pattern: a boundary case meets a binary classification. Nefdt (2026) organizes this debate through a 2 × 2 matrix crossing language with cognition, placing LLMs in a “missing quadrant” of language without cognition. But the binary frame undersells his own insights. His hedging on proxies, partial capacities, and qualified attributions points toward something the table can’t express: that language and cognition are cluster concepts with graded membership. Homeostatic property cluster (HPC) theory, with its projection-purpose analysis, offers a better framework. Under the shared label cognition, different projections track distinct property clusters – one centered on mechanism and substrate, another on functional capacity – that converge in humans and co-vary in non-human animals. LLMs break the co-variation, exposing an ambiguity that convergence in biological systems had kept invisible. The debate persists because disputants track different clusters under a shared label, and resists resolution because they treat cluster kinds as if they had essences. But the categories themselves aren’t fixed: LLMs are reshaping what “cognition” means, not just testing whether they have it.

The Sorites paradox arises from treating tolerance – the principle that tiny changes preserve vague predicates – as absolute rather than scale-dependent. Building on recent non-Archimedean treatments of vagueness (e.g. Itzhaki, 2021), this paper offers a particularly simple hyperreal model of scale-sensitive tolerance: changes that are infinitesimally small relative to the current scale preserve predicate application, while accumulated changes can cross boundaries. The model keeps classical bivalent logic throughout and, in a hyperfinite setting, locates sharp cutoffs at hypernatural indices that remain epistemically inaccessible. The contribution is methodological rather than revolutionary: I make explicit a Relative Tolerance postulate stated in terms of fractional change, combine it with a hyperfinite sorites chain that guarantees a least hypernatural cutoff, and package the resulting picture in a way that is accessible without deep background in nonstandard analysis. This shows that familiar commitments – monotonicity, scale-sensitive tolerance, clear positive and negative cases – are jointly satisfiable in a classical setting. The resulting framework is best seen as a scale-sensitive cousin of epistemicism, though the same machinery is compatible with contextualist, supervaluationist, and other approaches.