LING notes update

Author: localhost433

notes/courses/LING-UA-1/04-05-phonology.md

@@ -118,9 +118,9 @@ class PhonologicalAnalyzer {
      * @param s1
      * @param s2
      * @param lexicon  corpus
-     * @return         CONTRASTIVE → distinct phonemes
-     *                 COMPLEMENTARY → allophones of one phoneme
-     *                 FREE_VARIATION → context-overlap without meaning contrast
+     * @return         CONTRASTIVE -> distinct phonemes
+     *                 COMPLEMENTARY -> allophones of one phoneme
+     *                 FREE_VARIATION -> context-overlap without meaning contrast
      */
     public static DistributionType classifyPair(Sound s1,
                                                 Sound s2,

notes/courses/LING-UA-1/17-18-19-sociolinguistics.md

@@ -158,7 +158,7 @@ Overt prestige
     - Envelope of variation: Anywhere you can use a modal.
   - Double Objects
     - Variable: Object Constructions
-    - Variants: *‘a fish*’ vs. ‘*me a fish*’ etc. → ‘OBJ’ vs. ‘OBJ + OBJ'
+    - Variants: *‘a fish*’ vs. ‘*me a fish*’ etc. -> ‘OBJ’ vs. ‘OBJ + OBJ'
     - Envelope of variation: After transitive verbs
 
 ---

notes/courses/LING-UA-2/01-sounds.md

@@ -89,4 +89,6 @@ English contrasts stress positions: **OBject** (noun) vs. **obJECT** (verb); **C
 ### Common stress patterns
 - **Two syllables:** **Trochee** (first-syllable stress) *never, window, sprinkle*; **Iamb** (second-syllable stress) *indeed, perhaps, amuse*.
 - **Three syllables:** **Final** (*introduce, guarantee, comprehend*), **Penultimate** (*bandana, December, regardless*), **Antepenultimate** (*excellent, festival, happily*).
-> **Names & stress:** Try naming examples that fit each pattern; note why some shortened forms are impossible (*Jennifer -> Jen*, but not *Je*; *Alfred -> Al/Alf*, but not *Alfr*).
+> **Names & stress:** Try naming examples that fit each pattern; note why some shortened forms are impossible (*Jennifer -> Jen*, but not *Je*; *Alfred -> Al/Alf*, but not *Alfr*).
+
+These phonological tools show up throughout the course, especially in [Naming & Gender](https://robinc.vercel.app/note.html?course=LING-UA-2&note=03-gender) (sound cues to social categories) and [Nickname Formation](https://robinc.vercel.app/note.html?course=LING-UA-2&note=05-nickname) (clipping patterns and constraints).

notes/courses/LING-UA-2/02-change.md

@@ -18,43 +18,13 @@ date: 2025-09-16/18/23
 - Why do many families independently converge on similar name choices?  
 - What mechanisms could explain progression -> reversal -> slow cyclic return?
 
----
-
-# Name Blends - Portmanteaux of personal names
-
-**Idea**: Proper names can blend (e.g., *Kim Kardashian* + *Kanye West* -> **Kimye**), following phonological and orthographic pressures.
-
-**Probabilistic constraints (tendencies, not rules)**
-1. **Overlap**: Blend where sounds/spelling overlap.  
-2. **Onset conservation**: The name with the more complex onset tends to come first.  
-3. **Lexical neighborhood**: Prefer outputs that sound like existing words (avoid negative associations).  
-4. **Orthographic transparency**: Keep common sound↔spelling correspondences.  
-5. **Understandability**: Result should clearly reveal both sources (match syllable count or stress when helpful).
-
-**Try it**
-- Justify given celebrity blends using the constraints above.  
-- For pairs with two candidate blends, argue which is “better” and why.  
-- Create and defend your own blends for the provided pairs.
-
----
-
+ 
 # Trends in Name Data
 
-**Setup**
-- Copy the provided US and UK “Top 100/Top 10 by decade” sheets -> *File -> Make a copy*.  
-- Inspect structure (esp. the **Rank** column). Use *Data -> Create a filter*.
-
-**String utilities**
-- Extract edges: `=RIGHT(cell, n)` and `=LEFT(cell, n)` -> build **First letter** / **Last letters** helper columns.  
-- Fill formulas efficiently -> drag the **fill handle**.
-
-**Count/average by criteria (slow path)**
-- `=COUNTIFS(range1, "A", range2, "F", range3, "1904")` -> e.g., count girls’ names starting with **A** in 1904.  
-- `=AVERAGEIFS(rank_range, first_letter_range, "A", sex_range, "F", year_range, "1904")`.
-
 **Pivot tables (fast path)**
 - *Insert -> Pivot table* -> set **Rows -> Year**, optionally **Columns -> Sex**.  
-- **Values -> COUNTA** on a flag column (e.g., “Monarch name?”) to count; **Values -> AVERAGE** on **Rank** to track average rank over time.  
+- **Values -> COUNTA** on a flag column (e.g., “Monarch name?”) to count how many in each year.  
+- Or **Values -> AVERAGE** on **Rank** to track average rank over time.  
 - Chart it: select pivot output -> *Insert -> Chart*.
 
 **Mini-project**
@@ -63,6 +33,28 @@ date: 2025-09-16/18/23
 
 ---
 
+# Name Blends - Portmanteaux of personal names
+
+**Definition**
+- A **blend** is a new word formed by merging parts of two names (e.g., *Brangelina*).
+- Blends are a subtype of portmanteau formation; they are not just concatenation.
+
+**Observed constraints**
+- **Overlap is preferred** when possible.
+- **Onset conservation**: the beginning of the first name often remains intact.
+- **Prosodic well-formedness**: blends prefer plausible English syllable/stress patterns.
+- **Similarity / neighborhood effects**: blends tend to preserve recognizable pieces so the sources stay recoverable.
+
+**Examples**
+- *Renesmee* (Bella + Renee + Esme).
+- Celebrity couple names (*Kimye*, *Bennifer*).
+
+**Why they work**
+- They compress two identities into one label.
+- They feel “name-like” when they follow phonotactic and stress patterns.
+
+---
+
 ## References
 - Lieberson, *A Matter of Taste* (2000)  
 - Labov (2010) on diffusion and ratchet-like change

notes/courses/LING-UA-2/03-gender.md

@@ -15,6 +15,14 @@ date: 2025-09-25/30
 **Psycholinguistic evidence**
 - Listeners use stress/length/finality cues to infer a novel name’s gender; such knowledge parallels other category-sound correlations (e.g., noun/verb stress biases).
 
+**“Round” vs. “sharp” names and gendered perception**  
+Experimental tasks with novel names:  
+- “Round-sounding” names (e.g., /b, l, m, n, u, o, ɑ/) are more likely to be matched to round/soft characters and are perceived as more feminine.  
+- “Sharp-sounding” names (e.g., /k, p, t, i, e, ɛ, ʌ/) map to angular characters and are perceived as more masculine.  
+Hypothesis: cultural associations (e.g., size dimorphism) + articulatory/acoustic cues -> implicit gendered expectations.
+
+(These effects overlap with broader sound symbolism patterns, see [Naming & Sound Symbolism](https://robinc.vercel.app/note.html?course=LING-UA-2&note=04-symbolism).)
+
 ---
 
 # Trends

notes/courses/LING-UA-2/04-symbolism.md

@@ -55,14 +55,6 @@ Correlations between name phonology and character **size/power**:
 - Fewer sonorants / different place features track with evolutionary stage.  
 - In Japanese data, more **labial** consonants associate with *smaller/cuter* items (mirrors broader Japanese branding tendencies).
 
-**“Round” vs. “sharp” names and gendered perception**  
-Experimental tasks with novel names:  
-- “Round-sounding” names (e.g., /b, l, m, n, u, o, ɑ/) are more often judged as fitting round/soft characters and are perceived as more feminine.  
-- “Sharp-sounding” names (e.g., /k, p, t, i, e, ɛ, ʌ/) map to angular characters and are perceived as more masculine.  
-Hypothesis: cultural associations (e.g., size dimorphism) + articulatory/acoustic cues -> implicit gendered expectations.
-
----
-
 ## In-class activities
 
 1. **Bouba/Kiki on Names**  

notes/courses/LING-UA-2/05-nickname.md

@@ -92,7 +92,7 @@ French uses **truncation** and, in many cases, **reduplication**, governed by ro
      An [an] -> [na] -> **Nana**; Yves [iv] -> [vi] -> **Vivi**; Hugues [yɡ] -> [ɡy] -> **Gugu**.
 
 ## Probabilistic tendencies (French)
-- **Sound symbolism** sometimes exploited (e.g., “lighter” sequences like [ni] in **Nini**).  
+- Sometimes exploits sound symbolism (see [Naming & Sound Symbolism](https://robinc.vercel.app/note.html?course=LING-UA-2&note=04-symbolism), e.g., “lighter” sequences like [ni] in **Nini**).
 - **Avoid typologically unusual sounds**: replace/remove segments dispreferred cross-linguistically or in child acquisition.  
 - Many pressures mirror **child phonology** (preference for open syllables, fewer clusters).
 

notes/courses/LING-UA-2/06-history.md

@@ -67,7 +67,7 @@ In England, toponyms preserve **multiple settlement layers**:
 - When certain generics cluster in one area, they point to the **prior inhabitants’ language**.
 
 **Inference**
-- Spatial clustering of one language’s generics → region was once dominated by that language group.
+- Spatial clustering of one language’s generics -> region was once dominated by that language group.
 - Sudden changes/boundaries in place-name language can match **historical political or settlement boundaries**.
 
 ---
@@ -87,20 +87,10 @@ In England, toponyms preserve **multiple settlement layers**:
   - County names, townships, and landmarks commemorating heroes, battles, presidents, etc.
 - Named maps can show **paths of settlement**, ethnic enclaves, and changing political priorities.
 
----
-
-# Surnames as Parallel Evidence
-
-**Last names and history**
-- The most common **surnames** in a region also encode:
-  - Migration patterns (e.g. high concentration of a particular ethnic surname).
-  - Past colonization or slavery.
-  - Religious and linguistic history.
+ 
+# Surnames as Parallel Evidence (pointer)
 
-**Combined evidence**
-- **Toponyms + surnames** together provide a richer picture:
-  - Toponyms = long-term geographic memory.
-  - Surnames = more recent demographic patterns.
+If you want a general framing of how surnames can function as historical evidence (and how they complement toponyms), see [Names Around the World](https://robinc.vercel.app/note.html?course=LING-UA-2&note=07-world).
 
 ---
 

notes/courses/LING-UA-2/07-world.md

@@ -126,6 +126,19 @@ Geographic features:
 
 ---
 
+## Surnames as historical evidence
+
+**Last names and history**
+- The most common **surnames** in a region can encode:
+  - Migration patterns (for example, local concentration of a particular ethnic surname).
+  - Past colonization or slavery.
+  - Religious and linguistic history.
+
+**Combined evidence**
+- **Toponyms + surnames** together provide a richer picture (see [Names & Settlement History](https://robinc.vercel.app/note.html?course=LING-UA-2&note=06-history)):
+  - Toponyms = long-term geographic memory.
+  - Surnames = more recent demographic patterns.
+
 ## Given names and state regulation
 
 - Some states **regulate given names** to:

notes/courses/LING-UA-2/08-immigrant.md

@@ -64,9 +64,9 @@ Topics:
 
 2. **Americanization**
    - Choosing an English name that is a **loose phonetic match**:
-     - e.g. *Soo Fei → Fay*, *Wei Lim → William*, *Teong → Ted*, *Mei Guen → Mae Gwen*, *Yoon → June*.
+     - e.g. *Soo Fei -> Fay*, *Wei Lim -> William*, *Teong -> Ted*, *Mei Guen -> Mae Gwen*, *Yoon -> June*.
    - Surnames sometimes respelled to look more “English”:
-     - *Fan → Fann*, *Lam → Lamm*, *Lin → Lynn*, *Go → Goe*, *Hsu → Hsue*.
+     - *Fan -> Fann*, *Lam -> Lamm*, *Lin -> Lynn*, *Go -> Goe*, *Hsu -> Hsue*.
 
 3. **Translation**
    - Occasionally, children receive an English name that **translates** the meaning of the Chinese name (e.g. naming “Iris” for a Chinese name meaning ‘orchid’).
@@ -109,7 +109,7 @@ Topics:
 
 ### Pressures to conform
 
-- Qualitative work in Canadian cities (e.g. London, Ontario) identifies “forces of conformity”:
+- Qualitative work in Canadian cities (e.g. London, Ontario) identifies "forces of conformity":
   1. **Employment pressure** – belief that an “easier” or more Anglo-sounding name helps with hiring.
   2. **School & peer pressure** – desire not to stand out or be teased.
   3. **Avoiding constant explanations** – fatigue from spelling, correcting pronunciation, etc.
@@ -123,21 +123,9 @@ Topics:
 
 ---
 
-## Names and employment outcomes
-
-- Field experiments in Canada (e.g. Oreopoulos 2011):
-  - Researchers send **identical resumes** with different **name types**:
-    - Prototypically **“Canadian”** (Anglo) names.
-    - Chinese/Indian/Pakistani names with **Canadian education & experience**.
-    - Names with **foreign education**, mixed experience, etc.
-    - Mixed combinations (e.g. Anglo first + Chinese last name).
-  - Measure **callback rates** for job interviews.
-
-Key patterns:
-- Resumes with **Anglo names + Canadian credentials** receive the **highest callback rate** (~16%).
-- Adding a clearly **non-Anglo name**, even with Canadian education and experience, **reduces callbacks**.
-- Mixing Anglo first names with non-Anglo last names does **not fully remove** the penalty.
-- There is **no major difference** among different Asian-origin names (Chinese vs Indian vs Pakistani) in the study; they are collectively treated as “foreign”.
+## Names and employment outcomes (pointer)
+
+Audit-style evidence on name-based discrimination in hiring (resumes, email requests, callbacks) is summarized in [Naming & Bias](https://robinc.vercel.app/note.html?course=LING-UA-2&note=10-bias). This note focuses on the **pressures and strategies** that shape immigrant naming choices.
 
 ---