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Updated 16 December 2025

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A first name genderedness table is an empirical mapping from first names to gender-associated metrics—most commonly, probabilities or categorical labels indicating the likelihood a given name is used by males, females, or, in some frameworks, for individuals of another or indeterminate gender. These tables, based on population records, annotated datasets, curated expert judgments, or algorithmic inference, are essential data structures in computational social science, NLP, bibliometrics, bias auditing, and fairness research. Their construction, metrics, and intended applications vary substantially by methodological context, region, and sociolinguistic considerations.

1. Definitions, Metrics, and Taxonomic Structure

First name genderedness captures the empirical or inferred association between a first name and gender. The typical entries of a genderedness table include at minimum the name string and associated statistics or labels. Key quantitative metrics include:

Conditional probabilities: $P(\mathrm{male}\mid n)$ and $P(\mathrm{female}\mid n)$ , estimated from labeled datasets by $\frac{\mathrm{count}_\mathrm{male}(n)}{\mathrm{count}_\mathrm{male}(n) + \mathrm{count}_\mathrm{female}(n)}$ and its complement (Krstovski et al., 2023, Sainte-Marie et al., 9 Dec 2025, Bijary et al., 14 Sep 2025).
Genderedness score: Defined variously as $g(n) = |P_\mathrm{male}(n) - P_\mathrm{female}(n)|$ or as an index of bias (e.g., log-ratio), this scalar measures how strongly a name is associated with a gender (Krstovski et al., 2023, Hu et al., 2021, Sullivan et al., 2020, Herdağdelen, 2017).
Categorical assignments: Many tables append a “predicted gender” label or assign taxonomic categories such as “strongly gendered,” “ambiguous,” “conditionally gendered by country,” or “unknown” (Buskirk et al., 2022).

An example table structure:

Name	$P_\mathrm{male}(n)$	$P_\mathrm{female}(n)$	$g(n)$	Label
John	0.99	0.01	0.98	Strong male
Ashley	0.04	0.96	0.92	Strong female
Taylor	0.60	0.40	0.20	Ambiguous

(Krstovski et al., 2023, Sainte-Marie et al., 9 Dec 2025, Buskirk et al., 2022)

2. Sources and Construction Methodologies

Approaches to constructing genderedness tables differ with respect to data source, annotation policy, and algorithmic machinery:

Government and registry data: Large-scale demographic databases (e.g., US SSA, IBGE, INSEE) provide direct counts by sex at birth, enabling maximum-likelihood estimates of $P(\mathrm{gender}\mid n)$ (Krstovski et al., 2023, Misa, 2022, Sullivan et al., 2020, Sainte-Marie et al., 9 Dec 2025).
Expert curation: Some studies employ expert or native-speaker raters to assign binary labels, excluding ambiguous names to maximize reliability. However, this omits unisex and borderline cases (Sakunkoo et al., 15 Apr 2025).
Meta-learning and consensus: Ensemble strategies, exemplified by cultural consensus theory (CCT), aggregate $N$ disparate sources with differing reliability to estimate per-name consensus probabilities and assign taxonomic categories informed by entropy and source competence (Buskirk et al., 2022).
Algorithmic inference: Naïve–Bayes, regularized logistic regression, SVM, and neural classifiers use character n-grams or orthographic features to learn probabilistic associations and can generalize to unseen names (Zhao et al., 2019, Mueller et al., 2016, Hu et al., 2021).

Each methodology brings distinct tradeoffs in coverage, interpretability, and global applicability.

3. Statistical Definitions of Genderedness Indices

Quantitative indices of genderedness—central to both table construction and downstream analysis—fall into several families:

Absolute probability gap: $g(n) = |P_\mathrm{male}(n) - P_\mathrm{female}(n)|$ ; $g(n)\sim1$ implies a strongly gendered name, $g(n)\sim0$ signals ambiguous usage (Krstovski et al., 2023, Hu et al., 2021).
Normalized count imbalance: $G(n)=\frac{|f_n - m_n|}{f_n + m_n}$ (with $f_n$ , $m_n$ the total counts of female and male bearers, respectively). $G(n)=1$ for exclusively gendered names (Sullivan et al., 2020).
Log-odds or log-ratio: $G(n) = \log\left(\frac{P_\mathrm{male}(n)}{P_\mathrm{female}(n)}\right)$ , emphasizing direction and magnitude of bias (Herdağdelen, 2017).
Context-dependent score: For temporally indexed data, $G(n,t)$ reflects the gender probability for name $n$ at time $t$ . Such definitions are critical for quantifying “gender shift” in names over decades (Misa, 2022).
Entropy-based taxonomy: $H(n) = -z_n \log_2(z_n) - (1-z_n) \log_2(1-z_n)$ , with low entropy indicating strongly gendered names (Buskirk et al., 2022).

Explicit formulas, thresholding strategies, and smoothing approaches vary by implementation and corpus.

4. Handling Ambiguity, Temporal Drift, and Cultural Variation

Accurate gender assignment must address real-world ambiguities:

Unisex names: Ambiguous names (e.g., “Jordan,” “Taylor,” “Casey”) are characterized by low genderedness scores; many recent tables annotate these explicitly or use neural/voting ensemble refinement to improve accuracy on ambiguous cases (Krstovski et al., 2023, Buskirk et al., 2022, You et al., 7 Jul 2024).
Temporal drift: Names can shift in gender association across time (“Leslie problem”). $G(n, t)$ surfaces these trends. Studies highlight substantial net “female shift” for many names (e.g., “Leslie” from $p_F=0.08$ in 1925 to $p_F=0.80$ in 1975) (Misa, 2022). Ignoring this shift yields systematic bias in historical analyses.
Cultural and linguistic context: Name–gender mappings may be country- or language-specific—e.g., “Andrea” is male in Italy but female in English-speaking locales (Hu et al., 2021, Buskirk et al., 2022, Bijary et al., 14 Sep 2025). Taxonomies often include “conditionally gendered by country/decade” classes (Buskirk et al., 2022).

Robust tables require context metadata, explicit unisex/ambiguous handling, and, where possible, temporal and geographic granularity.

First name genderedness tables are critical infrastructure for several domains:

Bibliometrics and scientometrics: Used to merge author records and analyze citation bias as a function of name genderedness, revealing that masculinized names accrue citation advantages independent of author role (Sainte-Marie et al., 9 Dec 2025).
Bias and fairness auditing in LLMs: Evaluating LLM outputs, projections onto gender directions in embedding space closely track empirical gender probabilities. LLMs, however, show systematic underperformance on gender-neutral and non-English names (An et al., 9 Mar 2025, You et al., 7 Jul 2024).
Population surveys and policy: Name-based gender estimation enables gender-disaggregated analyses where direct gender information is unavailable, including social science surveys, public health studies, and digital demography (Krstovski et al., 2023).
Digital identity and user account assignment: Probabilistic gender prediction from names is integrated into user account systems (e.g., Open Gender Detection for Persian names), with further applications in chatbot personalization, pronoun resolution, and fairness audits in platform design (Bijary et al., 14 Sep 2025).
Interface design and bias: Genderedness distribution in name ordering (e.g., alphabetically sorted lists) produces measurable gender imbalance at the top- $k$ selection threshold (Sullivan et al., 2020).

6. Limitations and Considerations

Despite demonstrated utility, genderedness tables have inherent limitations:

Coverage gaps and suppression: Minimum frequency cutoffs, temporal censoring, and source-specific policies (e.g., suppression of rare names) lead to incomplete coverage and biased empirical estimates (Sullivan et al., 2020).
Non-binary identities: Most current tables are binary; attempts to introduce a “neutral”/“unisex” category expose further model inadequacies, with LLMs and lexicon methods underperforming on non-binary/neutral labels (<40% accuracy vs. >80% for binary cases) (You et al., 7 Jul 2024).
Algorithmic bias propagation: Universalizing present-day $P(\mathrm{female}\mid n)$ across all time periods induces systematic overcounting of women in historical data, particularly for names whose genderedness shifted substantially during the 20th century (Misa, 2022).
Cultural specificity and transliteration: Name–gender mapping validity degrades with transliteration (e.g., Persian), migration of name forms, or non-Western populations, necessitating resource-specific adaptation, normalization, and multi-source cross-validation (Bijary et al., 14 Sep 2025).

Ethical use requires transparency about limitations, explicit notation of “unknown” and “ambiguous” classes, and, where feasible, inclusion of contextual variables.

7. Representative Construction Recipes and Table Exemplars

Canonical recipes—grounded in the literature—demonstrate both closed-form and consensus-based estimation. For instance, using maximum-likelihood on population counts:

$P(\mathrm{female}\mid n) = \frac{\mathrm{count}_{\mathrm{female}}(n)}{\mathrm{count}_{\mathrm{female}}(n) + \mathrm{count}_{\mathrm{male}}(n)}$

(Krstovski et al., 2023, Sainte-Marie et al., 9 Dec 2025, Bijary et al., 14 Sep 2025)

CCT meta-learning steps:

$z_m = \frac{\prod_{n=1}^N [x_{nm} c_n + (1-x_{nm})(1-c_n)]}{\prod_{n=1}^N [x_{nm} c_n + (1-x_{nm})(1-c_n)] + \prod_{n=1}^N [x_{nm}(1-c_n) + (1-x_{nm})c_n]}$

(Buskirk et al., 2022)

And for illustration, an excerpted table:

Name	$P_\mathrm{male}$	$P_\mathrm{female}$	$g(n)$	Category
Mary	0.002	0.998	0.996	Strong female
John	0.987	0.013	0.974	Strong male
Taylor	0.600	0.400	0.200	Ambiguous

(Krstovski et al., 2023)

These structures, formulas, and threshold rules are directly replicable from primary sources, and can be adapted for domain-specific needs.

In sum, first name genderedness tables, as empirically structured resources, are foundational for research at the intersection of language, identity, demography, and computational social science. Their reliability, interpretability, and limitations are determined by transparency of construction, context modeling, source diversity, and explicit handling of ambiguity and drift. Ongoing methodological refinement—especially around non-binary, cross-temporal, and cross-cultural categories—remains a central research direction.