[DOCS] Remove binary gendered language (#48362)

docs/plugins/mapper-annotated-text.asciidoc

@@ -245,7 +245,7 @@ However, a problem arises if your named entity happens to be a single term and l
 company `elastic`. In this case, a search on the annotated text field for the token `elastic`
 may match a text document such as this:
 
-    he fired an elastic band
+    they fired an elastic band
 
 To avoid such false matches users should consider prefixing annotation values to ensure 
 they don't name clash with text tokens e.g.

docs/reference/aggregations/bucket/significantterms-aggregation.asciidoc

@@ -7,7 +7,7 @@ An aggregation that returns interesting or unusual occurrences of terms in a set
 * Suggesting "H5N1" when users search for "bird flu" in text
 * Identifying the merchant that is the "common point of compromise" from the transaction history of credit card owners reporting loss
 * Suggesting keywords relating to stock symbol $ATI for an automated news classifier
-* Spotting the fraudulent doctor who is diagnosing more than his fair share of whiplash injuries
+* Spotting the fraudulent doctor who is diagnosing more than their fair share of whiplash injuries
 * Spotting the tire manufacturer who has a disproportionate number of blow-outs
 
 In all these cases the terms being selected are not simply the most popular terms in a set.
@@ -388,7 +388,7 @@ By default this produces a score greater than zero and less than one.
 
 The benefit of this heuristic is that the scoring logic is simple to explain to anyone familiar with a "per capita" statistic. However, for fields with high cardinality there is a tendency for this heuristic to select the rarest terms such as typos that occur only once because they score 1/1 = 100%.
 
-It would be hard for a seasoned boxer to win a championship if the prize was awarded purely on the basis of percentage of fights won - by these rules a newcomer with only one fight under his belt would be impossible to beat.
+It would be hard for a seasoned boxer to win a championship if the prize was awarded purely on the basis of percentage of fights won - by these rules a newcomer with only one fight under their belt would be impossible to beat.
 Multiple observations are typically required to reinforce a view so it is recommended in these cases to set both `min_doc_count` and `shard_min_doc_count` to a higher value such as 10 in order to filter out the low-frequency terms that otherwise take precedence.
 
 [source,js]

docs/reference/search/request/track-total-hits.asciidoc

@@ -112,7 +112,7 @@ For instance the following response:
 \... indicates that the number of hits returned in the `total`
 is accurate.
 
-If the total number of his that match the query is greater than the
+If the total number of hits that match the query is greater than the
 value set in `track_total_hits`, the total hits in the response
 will indicate that the returned value is a lower bound:
 

x-pack/docs/en/security/authorization/mapping-roles.asciidoc

@@ -66,7 +66,7 @@ You can change this default behavior by changing the
 this is a common setting in Elasticsearch, changing its value might effect other
 schedules in the system.
 
-While the _role mapping APIs_ is he preferred way to manage role mappings, using
+While the _role mapping APIs_ is the preferred way to manage role mappings, using
 the `role_mappings.yml` file becomes useful in a couple of use cases: 
 
 . If you want to define fixed role mappings that no one (besides an administrator 

x-pack/docs/en/security/configuring-es.asciidoc

@@ -84,7 +84,7 @@ your subscription. For more information, see https://www.elastic.co/subscription
 +
 --
 For example, to grant _John Doe_ full access to all indices that match
-the pattern `events*` and enable him to create visualizations and dashboards
+the pattern `events*` and enable them to create visualizations and dashboards
 for those indices in {kib}, you could create an `events_admin` role
 and assign the role to a new `johndoe` user.