u/xFrankino

Ciao a tutti!

Volevo chiedervi se conosceste un posto per un'escursione, magari anche con zona picnic, non molto affollata perché vorrei andarci di domenica, e vorrei evitare famiglie ecc.

Magari anche un posto con un po' di riserva naturale:)

Potreste anche dirmi come raggiungerlo? Grazie:)

Hello everyone,

I am finishing a MA in Italy in Linguistics (with a thesis in LLMs, although the degree is not computational but humanistic only), and I have no clue what to do with my life (that's the new thing with our generation lol)

However, I said to myself that if I find nothing after I get my degree in September I will move abroad, just bc I've always wanted to move in a German speaking country (luckily, I have a B1 level, almost B2 -gotta pass the exam tho-). Since I love linguistics and LLMs I thought of this MA, idc if I have to start over bc I know that I will regret it if I spend another year doing nothing. I'd start in April.

My question is: is it worth it to move with a MA from another country to do another MA? I mean, can I start a new life with that or is it a waste of time bc the degree is nothing serious (in Italy many degrees are useless, almost all of them).

Another worry I have: is there the chance to find a part time job, that isn't something like a waiter? I mean, I'm totally fine working as a waiter, I actually enjoy it as I did it for 5 years here in Italy, but since I have a 'degree' and speak Italian and English at a C2 level, do you know if this can help me or I have to presss the replay button again?

Final question: how expensive can be the rent?

Thanks to anyone who will answer to this long list of questions, I appreciate <3

Hello everyone,

I am doing my MA thesis and is about LLM and detection of some linguistic strategies in some txt files.. I asked Copilot to build a script so I can detect patterns quickly, but when I run it many of the elements are not found (there are in the text and I added them also in the list of elements to find, but in the results there are none)

Below the script

import spacy

import os

import re

nlp = spacy.load("en_core_web_lg")

Then there is a list of nominalizations and impersonal patterns, then

def count_all_passives(doc):
    count = 0

    ABSTRACT_SUBJECTS = {
        "conflict","crisis","war","violence","tension","tensions",
        "situation","hostilities","fighting","escalation","unrest",
        "instability","suffering","destruction","turmoil"
    }

    sentences = list(doc.sents)

    for sent in sentences:
        sent_has_deagentivity = False

        # A) Passive
        for token in sent:
            if token.dep_ == "auxpass" and token.head.tag_ == "VBN":
                sent_has_deagentivity = True
                break

        # Abstract nouns
        if not sent_has_deagentivity:
            for token in sent:
                if token.dep_ in ("nsubj","nsubj:pass") and token.lemma_.lower() in ABSTRACT_SUBJECTS:
                    sent_has_deagentivity = True
                    break

        if sent_has_deagentivity:
            count += 1

    # normalization for 5 sentences
    if len(sentences) == 0:
        return 0

    return count / (len(sentences) / 5)



# ---- B. Nominalizations----
def count_nominalizations(doc, debug=False):
    sentences = list(doc.sents)
    nominal_count = 0

    for token in doc:
        if token.pos_ == "NOUN" and token.lemma_ in NOMINALIZATIONS:
           #Exclude non neutralised subject
            if token.dep_ not in ("nsubj", "nsubj:pass"):
                nominal_count += 1
                if debug:
                    print("Nominalization found:", token.text, "| dep:", token.dep_)

    if len(sentences) == 0:
        return 0

    return nominal_count / (len(sentences) / 5)


# ---- C. Impersonal pattern ----
def count_epistemic_modality(text, doc):
    count = 0
    text_lower = text.lower()

    for pattern in IMPERSONAL_PATTERNS:
        if re.search(pattern, text_lower):
            count += 1

    sentences = list(doc.sents)
    if len(sentences) == 0:
        return 0

    return count / (len(sentences) / 5)


# ============================
# 4. Analysis all files
# ============================

def analyze_folder(folder="."):
    results = []

    files = [f for f in os.listdir(folder) if f.endswith(".txt")]

    for filename in files:
        path = os.path.join(folder, filename)
        with open(path, "r", encoding="utf-8") as f:
            text = f.read()

        doc = nlp(text)

        P = count_all_passives(doc)
        N = count_nominalizations(doc)
        M = count_epistemic_modality(text, doc)

        results.append({"file": filename, "P": P, "N": N, "M": M})

    # Min–max scaling
    P_values = [r["P"] for r in results]
    N_values = [r["N"] for r in results]

    P_min, P_max = min(P_values), max(P_values)
    N_min, N_max = min(N_values), max(N_values)

    for r in results:
        r["P_norm"] = 0 if P_max == P_min else (r["P"] - P_min) / (P_max - P_min)
        r["N_norm"] = 0 if N_max == N_min else (r["N"] - N_min) / (N_max - N_min)
        r["M_norm"] = r["M"]

        r["INS"] = (r["P_norm"] + r["N_norm"] + r["M_norm"]) / 3

        if r["INS"] >= 0.60:
            r["risk"] = "HIGH — simulated objectivity"
        elif r["INS"] >= 0.30:
            r["risk"] = "MEDIUM"
        else:
            r["risk"] = "LOW"

    return results


# ============================
# 5. Execution
# ============================

if __name__ == "__main__":
    results = analyze_folder(".")

    print("\n=== INS — Index of Neutralization Strategies ===\n")
    for r in results:
        print(f"FILE: {r['file']}")
        print(f"  P_norm: {r['P_norm']:.3f}")
        print(f"  N_norm: {r['N_norm']:.3f}")
        print(f"  M_norm: {r['M_norm']:.3f}")
        print(f"  INS:    {r['INS']:.3f}")
        print(f"  RISK:   {r['risk']}")
        print("")