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Enregistrement W7078112481 · doi:10.5281/zenodo.16967401

Overexposure, Overconfidence, and the Making of India Fatigue

2025· article· en· W7078112481 sur OpenAlexaboutno aff

Notice bibliographique

RevueZenodo (CERN European Organization for Nuclear Research) · 2025
Typearticle
Langueen
DomaineComputer Science
ThématiqueGeochemistry and Geologic Mapping
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésDiasporaPoliticsStereotype (UML)PhenomenonPrejudice (legal term)Stereotype threatXenophobiaExpatriateEthnic group

Résumé

récupéré en direct d'OpenAlex

Abstract The phenomenon of India Fatigue—a growing global skepticism, irritation, and backlash toward India’s demographic rise, cultural assertiveness, and political positioning—has emerged as a complex challenge in international relations. Using economic, sociological, and anthropological perspectives, this paper unpacks its drivers: overexposure of national narratives, reputational fallout from diaspora-linked scandals, perceived cultural overreach, media amplification, and mismatches between India’s self-image and external perception. Mechanisms of backlash, including stereotype reinforcement, reactive ethnicity, and counter-hegemonic resistance, are examined alongside high-impact case studies. Scenario projections, including the implications of a hypothetical 50% U.S. tariff on Indian goods, highlight strategic vulnerabilities. The study concludes with targeted recommendations to recalibrate soft power, strengthen diaspora engagement, and mitigate reputational damage while preserving legitimate national interests. Keywords India Fatigue; diaspora relations; soft power backlash; cultural hegemony; Vishwaguru; Vishwamitra; fraud salience; stereotype amplification; international relations; policy recalibration. Introduction The phenomenon of "India fatigue" describes a growing global skepticism, irritation, and pushback toward India's rapid demographic, cultural, and political assertiveness, particularly in countries with significant Indian diaspora populations. This complex sentiment arises from a confluence of factors, including assertive national narratives, increased diaspora visibility, reported misconduct, and local anxieties about belonging, fairness, and power. Xenophobia and racial prejudice are blanket hostilities toward outsiders or toward people perceived to belong to a certain race or ethnicity and are different concepts from that of fatigue. They’re rooted in identity‑based bias—often applying indiscriminately to all foreign‑born people or all members of a visible minority, regardless of behavior, context, or national policy. By contrast, India Fatigue—as the term is used in policy and media analysis—refers to something more situational and actor‑specific: • It’s about a gradual weariness or irritation that develops in response to a particular country’s overexposure in politics, economics, culture, and media—in this case, India. • It often emerges even in societies that don’t have deep‑seated racial hostility toward Indians but feel saturated or strained by certain patterns: constant civilizational self‑branding, high‑profile scandals, aggressive lobbying, or perceived double standards. • The sentiment can coexist with admiration or respect for other aspects of India; it isn’t always rooted in “us vs. them” identity conflict. • While xenophobia is identity‑first (“you don’t belong here”), fatigue is often narrative‑ or conduct‑first (“we’ve heard this too much / this behavior is wearing thin”). • Crucially, India Fatigue can be voiced even by people of Indian origin within the diaspora, which doesn’t fit the usual xenophobia pattern. Think of it as the difference between systemic aversion to outsiders and situational pushback against a specific country’s soft power and diaspora dynamics. They can overlap—in some contexts fatigue rhetoric can mask deeper xenophobia—but analytically, the term “India Fatigue” signals that the critique is aimed at perceived overreach, not the mere presence of Indians. Countries reporting phenomena similar to "Indian Fatigue"—a frustration or exhaustion linked to behaviors of Indian tourists, immigrants, or large Indian demographic groups—include: Canada: Indian Fatigue is widely discussed, especially in cities like Toronto, Vancouver, Montreal, Calgary, and Ottawa, due to rapid demographic and cultural changes from Indian immigration United Kingdom: Areas with large Indian communities show signs of cultural clustering leading to local resentment Australia: Similar complaints about lack of integration and visible Indian-only services and shops. Middle Eastern countries (e.g., UAE, Dubai): Frequent complaints about poor public hygiene and lack of respect for local customs by some Indian workers and visitors.youtube Southeast Asian countries (Thailand, Bali, Singapore): Local communities report disruptive tourist behaviors such as loud music, littering, aggressive haggling, and rule ignoring. Nepal: Local businesses have protested Indian visitor behavior. This pattern is seen globally in places with substantial Indian tourist or immigrant populations, reflecting cultural clashes and difficulties adapting to host country norms, fueling the "Indian Fatigue" phenomenon.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,005
score de la tête « metaresearch » (Gemma)0,009
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: aucune
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,027

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0050,009
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0020,001
Études des sciences et des technologies0,0090,017
Communication savante0,0090,004
Science ouverte0,0010,008
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0040,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,026
Tête enseignante GPT0,246
Écart entre enseignants0,220 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2025
Routes d'admission1
Résumé présentoui

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