Documentation

Linglib.Studies.Scontras2014

[Sco14] — The Semantics of Measurement #

[Chi98] [Kri89] [Sco14] [Zab05]

Empirical observations and bridge theorems for Scontras's quantizing noun typology (Ch. 3).

Key Empirical Claim #

The three classes of quantizing nouns differ systematically in whether they license a MEASURE reading (Scontras Ch. 3, Table 3.5 p. 89). The MEASURE reading is the one in which a quantizing noun functions as a unit-name for the substance, rather than denoting the substance's containers or atoms.

Diagnostics for the MEASURE/CONTAINER ambiguity #

Container nouns can be disambiguated:

Architecture #

This file encodes empirical observations and proves that the Fragment entries (class assignments) correctly predict the Theory's MEASURE-reading licensing.

Dependency chain: Theory (licensesMeasureReading) → Fragment (QuantizingNounEntry.nounClass) → Studies (this file)

An observed MEASURE-licensing judgment for a quantizing noun in a specific context.

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        "Three kilos of rice" — a measure of rice.

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            "Three glasses of water in the cupboard" — three individual glass-objects. The CONTAINER reading is forced by the locative; MEASURE is unavailable.

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                "Add three glasses of water" — a 3-glass-volume quantity of water. The MEASURE reading is forced by the recipe context.

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                    "Three grains of rice" — three rice-grain individuals. Atomizers' semantics is inherently relational and partitioning (Scontras eqs. (77), (87), pp. 89-90): grain takes the substance noun rice and imposes a partition into self-connected rice-atoms via π. The atoms are then counted by CARD (Scontras p. 100). MEASURE-reading fails because the semantics is partitioning rather than measure-naming.

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                            The central bridge #

                            The Fragment assigns each noun a nounClass (from the Theory's QuantizingNounClass). The Theory defines licensesMeasureReading mapping class + reading to a MEASURE-licensing prediction (Scontras Table 3.5 p. 89). We prove that this prediction matches the empirical observation for EVERY example in our data.

                            This is the payoff of the Theories → Fragments → Studies architecture: if someone changes a noun's class assignment in the Fragment, or changes the licensesMeasureReading function in the Theory, the bridge theorems break.

                            The Theory's MEASURE-licensing prediction matches the empirical observation for every example in our data set.

                            For measure term observations: the Theory predicts MEASURE = true.

                            For atomizer observations: the Theory predicts MEASURE = false (atomizers are counted by CARD, not measured).

                            For container noun observations: MEASURE-licensing depends on the reading. CONTAINER → not MEASURE; MEASURE → MEASURE.

                            Fragment consistency #

                            We also verify that the Fragment entries used in our observations have the same class assignment as the observations themselves. This catches the case where someone defines glass.nounClass :=.atomizer in the Fragment but uses .containerNoun in the observation.

                            The Fragment's grain entry matches the observation's class.

                            The Fragment's drop entry matches the observation's class.

                            Disambiguation contexts for container nouns (Scontras Ch. 3 §3.2.1).

                            A sentence context can force one reading of an ambiguous container noun:

                            • Locative PPs ("in the cupboard") → CONTAINER (the physical objects are located)
                            • Recipe/instruction context → MEASURE (amount of substance)
                            • Demonstratives ("those three glasses") → CONTAINER (individuated)
                            • Generic quantity context ("add three glasses") → MEASURE
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                                          All disambiguation contexts involve container nouns (not measure terms or atomizers — only container nouns are ambiguous).

                                          Combining disambiguation with the licensing prediction: recipe contexts yield MEASURE, locative contexts yield non-MEASURE.